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sccastillo commited on Aug 1

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README.md +31 -4
app.py +632 -212
research_team.py +148 -104

README.md CHANGED Viewed

@@ -15,13 +15,40 @@ Scientific research FastAPI application deployed on Hugging Face Spaces.
 ## Features
-- FastAPI web application
-- Docker-based deployment
-- Simple API endpoints
 ## API Endpoints
-- `GET /` - Returns a greeting message
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

 ## Features
+- FastAPI web application with AI integration
+- Research Team for Claims Anchoring and Reference Formatting
+- Modern web interface with sidebar navigation
+- Docker-based deployment for Hugging Face Spaces
+- Comprehensive API endpoints
+## Web Interface
+The application features a modern, responsive web interface with:
+- **Sidebar Navigation**: Switch between AI Question Generator and Research Team Document Processor
+- **Health Monitoring**: Real-time API health checks and status monitoring
+- **Interactive Results**: Formatted display of research results with metrics and raw data
+- **Responsive Design**: Works on desktop and mobile devices
+## Quick Start
+```bash
+# Install dependencies
+pip install -r requirements.txt
+# Start the application
+uvicorn app:app --host 0.0.0.0 --port 8000 --reload
+```
+The web interface will be available at: http://localhost:8000
 ## API Endpoints
+- `GET /` - Returns HTML interface
+- `GET /api/hello` - Returns a JSON greeting message
+- `GET /api/health` - Health check endpoint
+- `POST /api/generate` - AI question answering
+- `POST /api/research/process` - Document processing with Research Team
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py CHANGED Viewed

@@ -1,14 +1,12 @@
 import os
-from fastapi import FastAPI, HTTPException, UploadFile, File
 from fastapi.responses import HTMLResponse
 from pydantic import BaseModel
 from dotenv import load_dotenv
 import asyncio
-# Importar dependencias de LangChain y OpenAI
-from langchain_openai import OpenAI, ChatOpenAI
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
 # Import ResearchTeam
 from research_team import create_research_team
@@ -48,6 +46,259 @@ def get_research_team():
         research_team = create_research_team()
     return research_team
 def answer_question(question: str):
     """
     Función para responder preguntas usando OpenAI LLM
@@ -56,239 +307,371 @@ def answer_question(question: str):
         raise HTTPException(status_code=400, detail="Please provide a question.")
     # Obtener API key de OpenAI desde variables de entorno
-    openai_api_key = os.getenv("OPENAI_API_KEY")
-    if not openai_api_key or openai_api_key == "your_openai_api_key_here":
-        raise HTTPException(status_code=500, detail="OpenAI API key not configured")
-    # Template simple para responder preguntas
-    prompt_template = PromptTemplate(
-        template="Answer the following question clearly and concisely: {question}",
-        input_variables=["question"]
-    )
-    # Inicializar OpenAI LLM
     try:
-        llm = OpenAI(
-            api_key=openai_api_key,
-            temperature=0.7
-        )
-        #llm = ChatOpenAI(
-        #    model="openai/gpt-4.1",
-        #    temperature=0.7,
-        #    api_key=os.getenv("GEAI_API_KEY"),
-        #    base_url=os.getenv("GEAI_BASE_URL")
-        #)
-        #llm = ChatOpenAI(
-        #    model="openai/gpt-4.1",
-        #    temperature=0.7,
-        #    api_key=os.getenv("GEAI_API_KEY"),
-        #    base_url=os.getenv("GEAI_BASE_URL")
-        #)
-        # Crear cadena LLM
-        llm_chain = LLMChain(
-            prompt=prompt_template,
-            llm=llm
         )
-        # Generar respuesta
-        response = llm_chain.run(question=question)
         return GenerateResponse(text=response.strip())
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Error generating response: {str(e)}")
-@app.get("/", response_class=HTMLResponse)
-def read_root():
-    """
-    Endpoint principal que devuelve una página HTML simple
-    """
-    html_content = """
     <!DOCTYPE html>
     <html>
     <head>
         <title>SciResearch API</title>
         <style>
-            body { font-family: Arial, sans-serif; margin: 40px; }
-            h1 { color: #333; }
-            .container { max-width: 800px; margin: 0 auto; }
-            .form-group { margin: 20px 0; }
-            .section { border: 1px solid #ddd; padding: 20px; margin: 20px 0; border-radius: 5px; }
-            input[type="text"] { width: 100%; padding: 10px; margin: 5px 0; }
-            textarea { width: 100%; padding: 10px; margin: 5px 0; height: 150px; }
-            button { background-color: #4CAF50; color: white; padding: 10px 20px; border: none; cursor: pointer; margin: 5px; }
-            button:hover { background-color: #45a049; }
-            .research-button { background-color: #2196F3; }
-            .research-button:hover { background-color: #1976D2; }
-            #response, #research-response { background-color: #f9f9f9; padding: 15px; margin-top: 20px; border-left: 4px solid #4CAF50; }
-            #research-response { border-left-color: #2196F3; }
-            .result-section { margin: 10px 0; padding: 10px; background-color: #f5f5f5; }
-            .loading { color: #666; font-style: italic; }
         </style>
     </head>
     <body>
-        <div class="container">
-            <h1>🦀 SciResearch API with Research Team</h1>
-            <p>¡Bienvenido a la aplicación de investigación científica con IA y equipo de research para análisis de documentos!</p>
-            <div class="section">
-                <h3>💬 Pregunta a la IA:</h3>
-                <div class="form-group">
-                    <input type="text" id="question" placeholder="Escribe tu pregunta aquí..." />
-                    <button onclick="askQuestion()">Preguntar</button>
                 </div>
-                <div id="response" style="display:none;">
-                    <h4>Respuesta:</h4>
-                    <p id="answer"></p>
                 </div>
             </div>
-            <div class="section">
-                <h3>📄 Research Team - Claims Anchoring & Reference Formatting:</h3>
-                <div class="form-group">
-                    <textarea id="document" placeholder="Pega aquí el contenido del documento para analizar claims y referencias..."></textarea>
-                    <button class="research-button" onclick="processDocument()">Procesar Documento</button>
                 </div>
-                <div id="research-response" style="display:none;">
-                    <h4>Resultados del Research Team:</h4>
-                    <div id="research-results"></div>
                 </div>
             </div>
-            <h2>Endpoints disponibles:</h2>
-            <ul>
-                <li><a href="/docs">/docs</a> - Documentación interactiva de la API</li>
-                <li><a href="/api/hello">/api/hello</a> - Saludo JSON</li>
-                <li><a href="/api/health">/api/health</a> - Estado de la aplicación</li>
-                <li><strong>/api/generate</strong> - Generar respuestas con IA (POST)</li>
-                <li><strong>/api/research/process</strong> - Procesar documento con Research Team (POST)</li>
-            </ul>
         </div>
-        <script>
-        async function askQuestion() {
-            const question = document.getElementById('question').value;
-            if (!question.trim()) {
-                alert('Por favor escribe una pregunta');
-                return;
-            }
-            try {
-                const response = await fetch('/api/generate', {
-                    method: 'POST',
-                    headers: {
-                        'Content-Type': 'application/json',
-                    },
-                    body: JSON.stringify({question: question})
-                });
-                const data = await response.json();
-                if (response.ok) {
-                    document.getElementById('answer').textContent = data.text;
-                    document.getElementById('response').style.display = 'block';
-                } else {
-                    alert('Error: ' + data.detail);
-                }
-            } catch (error) {
-                alert('Error de conexión: ' + error.message);
-            }
-        }
-        async function processDocument() {
-            const document_content = document.getElementById('document').value;
-            if (!document_content.trim()) {
-                alert('Por favor pega el contenido del documento');
-                return;
-            }
-            // Show loading state
-            const resultsDiv = document.getElementById('research-results');
-            resultsDiv.innerHTML = '<p class="loading">Procesando documento... Esto puede tomar unos minutos.</p>';
-            document.getElementById('research-response').style.display = 'block';
-            try {
-                const response = await fetch('/api/research/process', {
-                    method: 'POST',
-                    headers: {
-                        'Content-Type': 'application/json',
-                    },
-                    body: JSON.stringify({document_content: document_content})
-                });
-                const data = await response.json();
-                if (response.ok) {
-                    displayResearchResults(data.result);
-                } else {
-                    resultsDiv.innerHTML = '<p style="color: red;">Error: ' + data.detail + '</p>';
-                }
-            } catch (error) {
-                resultsDiv.innerHTML = '<p style="color: red;">Error de conexión: ' + error.message + '</p>';
-            }
-        }
-        function displayResearchResults(result) {
-            const resultsDiv = document.getElementById('research-results');
-            let html = '';
-            // Document metadata
-            if (result.document_metadata) {
-                html += '<div class="result-section">';
-                html += '<h4>📋 Metadatos del Documento:</h4>';
-                html += '<p><strong>Producto:</strong> ' + (result.document_metadata.product || 'No detectado') + '</p>';
-                html += '<p><strong>Países:</strong> ' + (result.document_metadata.countries?.join(', ') || 'No detectados') + '</p>';
-                html += '<p><strong>Idioma:</strong> ' + (result.document_metadata.language || 'No detectado') + '</p>';
-                html += '</div>';
-            }
-            // Claims analysis
-            if (result.claims_analysis) {
-                html += '<div class="result-section">';
-                html += '<h4>🔍 Análisis de Claims:</h4>';
-                html += '<p><strong>Total de Claims:</strong> ' + result.claims_analysis.total_claims + '</p>';
-                html += '<p><strong>Claims Principales:</strong> ' + result.claims_analysis.core_claims_count + '</p>';
-                html += '</div>';
-            }
-            // Claims anchoring
-            if (result.claims_anchoring) {
-                html += '<div class="result-section">';
-                html += '<h4>⚓ Claims Anchoring:</h4>';
-                if (result.claims_anchoring.summary) {
-                    const summary = result.claims_anchoring.summary;
-                    html += '<p><strong>Claims Procesados:</strong> ' + summary.total_claims_processed + '</p>';
-                    html += '<p><strong>Validados Exitosamente:</strong> ' + summary.successfully_validated + '</p>';
-                    html += '<p><strong>Tasa de Validación:</strong> ' + Math.round(summary.validation_rate * 100) + '%</p>';
-                }
-                html += '</div>';
-            }
-            // Reference formatting
-            if (result.reference_formatting) {
-                html += '<div class="result-section">';
-                html += '<h4>📚 Formateo de Referencias:</h4>';
-                html += '<p><strong>Referencias Formateadas:</strong> ' + result.reference_formatting.total_references + '</p>';
-                html += '</div>';
-            }
-            resultsDiv.innerHTML = html;
-        }
-        // Permitir envío con Enter
-        document.getElementById('question').addEventListener('keypress', function(e) {
-            if (e.key === 'Enter') {
-                askQuestion();
-            }
-        });
-        </script>
     </body>
     </html>
     """
-    return html_content
 @app.get("/api/hello")
 def greet_json():
@@ -302,13 +685,13 @@ def health_check():
     """
     Endpoint para verificar el estado de la aplicación
     """
-    openai_configured = bool(os.getenv("OPENAI_API_KEY")) and os.getenv("OPENAI_API_KEY") != "your_openai_api_key_here"
     return {
         "status": "healthy",
         "service": "sciresearch",
         "version": "1.0.0",
-        "openai_configured": openai_configured,
         "research_team_available": True
     }
@@ -319,6 +702,43 @@ def inference(request: QuestionRequest):
     """
     return answer_question(question=request.question)
 @app.post("/api/research/process", summary="Process document with Research Team", tags=["Research Team"], response_model=ResearchResponse)
 async def process_document_research(request: DocumentRequest):
     """

 import os
+from fastapi import FastAPI, HTTPException, UploadFile, File, Form
 from fastapi.responses import HTMLResponse
 from pydantic import BaseModel
 from dotenv import load_dotenv
 import asyncio
+# Importar dependencias de OpenAI
+from openai import OpenAI
 # Import ResearchTeam
 from research_team import create_research_team
         research_team = create_research_team()
     return research_team
+def get_html_with_response(question: str, answer: str, status: str, error_msg: str = None):
+    """Generate HTML page with AI response"""
+    response_content = ""
+    if status == "success" and answer:
+        response_content = f'''
+        <div class="response-section">
+            <h4>🤖 AI Response:</h4>
+            <div style="margin-bottom: 15px;">
+                {answer.replace(chr(10), '<br>')}
+            </div>
+        </div>
+        '''
+    elif status == "error":
+        response_content = f'''
+        <div class="response-section error">
+            <h4>❌ Error:</h4>
+            <div style="color: red;">
+                {error_msg or "Unknown error occurred"}
+            </div>
+        </div>
+        '''
+    return get_base_html("ai-generator", question, "", response_content, "")
+def get_html_with_research_response(document: str, result: dict, status: str, error_msg: str = None):
+    """Generate HTML page with research response"""
+    response_content = ""
+    if status == "success" and result:
+        response_content = f'''
+        <div class="response-section">
+            <h4>📊 Research Team Results:</h4>
+            {format_research_results(result)}
+        </div>
+        '''
+    elif status == "error":
+        response_content = f'''
+        <div class="response-section error">
+            <h4>❌ Error:</h4>
+            <div style="color: red;">
+                {error_msg or "Unknown error occurred"}
+            </div>
+        </div>
+        '''
+    return get_base_html("research-team", "", document, "", response_content)
+def format_research_results(result: dict) -> str:
+    """Format research results as HTML"""
+    html = ""
+    # Handle new structure with detailed_analysis and summary_statistics
+    summary_stats = result.get("summary_statistics", {})
+    detailed_analysis = result.get("detailed_analysis", {})
+    # DETAILED ANALYSIS SECTION (Priority Content)
+    if detailed_analysis:
+        html += f'''
+        <div style="margin-bottom: 30px;">
+            <h5>📋 Detailed Analysis Results</h5>
+        '''
+        # Claims Extracted Details
+        if "claims_extracted" in detailed_analysis:
+            claims_data = detailed_analysis["claims_extracted"]
+            all_claims = claims_data.get("all_claims", [])
+            core_claims = claims_data.get("core_claims", [])
+            html += f'''
+            <div style="background: white; padding: 20px; border-radius: 8px; border: 1px solid #e1e5e9; margin-bottom: 20px;">
+                <h6>🔍 Claims Extraction</h6>
+                <div style="margin-bottom: 15px;">
+                    <strong>Total Claims Found:</strong> {len(all_claims)} | <strong>Core Claims:</strong> {len(core_claims)}
+                </div>
+                <details style="margin-bottom: 10px;">
+                    <summary style="cursor: pointer; font-weight: bold;">View All Claims ({len(all_claims)})</summary>
+                    <div style="margin-top: 10px; max-height: 300px; overflow-y: auto;">
+            '''
+            for i, claim in enumerate(all_claims[:10]):  # Show first 10 claims
+                claim_type_color = {"core": "#e74c3c", "supporting": "#f39c12", "contextual": "#3498db"}.get(claim.get("type", "contextual"), "#95a5a6")
+                html += f'''
+                        <div style="padding: 10px; margin: 5px 0; border-left: 4px solid {claim_type_color}; background: #f8f9fa;">
+                            <strong>Claim {claim.get('id', i+1)}:</strong> {claim.get('text', '')[:200]}{'...' if len(claim.get('text', '')) > 200 else ''}<br>
+                            <small style="color: #666;">Type: {claim.get('type', 'unknown').title()} | Score: {claim.get('importance_score', 0)}</small>
+                        </div>
+                '''
+            if len(all_claims) > 10:
+                html += f'<div style="text-align: center; color: #666; margin-top: 10px;">... and {len(all_claims) - 10} more claims</div>'
+            html += '''
+                    </div>
+                </details>
+            </div>
+            '''
+        # Anchoring Results Details
+        if "anchoring_results" in detailed_analysis:
+            anchoring_data = detailed_analysis["anchoring_results"]
+            claims_with_evidence = anchoring_data.get("claims_with_evidence", [])
+            html += f'''
+            <div style="background: white; padding: 20px; border-radius: 8px; border: 1px solid #e1e5e9; margin-bottom: 20px;">
+                <h6>⚓ Claims Anchoring & Evidence</h6>
+                <details style="margin-bottom: 10px;">
+                    <summary style="cursor: pointer; font-weight: bold;">View Anchoring Results ({len(claims_with_evidence)})</summary>
+                    <div style="margin-top: 10px; max-height: 400px; overflow-y: auto;">
+            '''
+            for claim_evidence in claims_with_evidence:
+                status_color = {"validated": "#27ae60", "partial": "#f39c12", "unsupported": "#e74c3c"}.get(claim_evidence.get("validation_status", "unknown"), "#95a5a6")
+                html += f'''
+                        <div style="padding: 15px; margin: 10px 0; border: 1px solid #e1e5e9; border-radius: 8px;">
+                            <div style="display: flex; align-items: center; margin-bottom: 10px;">
+                                <strong>Claim {claim_evidence.get('claim_id', '')}:</strong>
+                                <span style="margin-left: 10px; padding: 4px 8px; background: {status_color}; color: white; border-radius: 4px; font-size: 12px;">
+                                    {claim_evidence.get('validation_status', 'unknown').title()}
+                                </span>
+                            </div>
+                            <div style="margin-bottom: 10px; color: #333;">
+                                {claim_evidence.get('claim_text', '')[:300]}{'...' if len(claim_evidence.get('claim_text', '')) > 300 else ''}
+                            </div>
+                            <div style="margin-bottom: 10px;">
+                                <strong>Supporting Evidence:</strong> {len(claim_evidence.get('supporting_evidence', []))} passages found
+                            </div>
+                            <div style="margin-bottom: 10px;">
+                                <strong>References:</strong> {len(claim_evidence.get('anchored_references', []))} references anchored
+                            </div>
+                            {f'<div style="font-size: 12px; color: #666;"><strong>Quality Assessment:</strong> {claim_evidence.get("quality_assessment", "")}</div>' if claim_evidence.get("quality_assessment") else ''}
+                        </div>
+                '''
+            html += '''
+                    </div>
+                </details>
+            </div>
+            '''
+        # Formatted References Details
+        if "formatted_references" in detailed_analysis:
+            ref_data = detailed_analysis["formatted_references"]
+            reference_details = ref_data.get("reference_details", [])
+            html += f'''
+            <div style="background: white; padding: 20px; border-radius: 8px; border: 1px solid #e1e5e9; margin-bottom: 20px;">
+                <h6>📚 Formatted References</h6>
+                <details style="margin-bottom: 10px;">
+                    <summary style="cursor: pointer; font-weight: bold;">View Formatted References ({len(reference_details)})</summary>
+                    <div style="margin-top: 10px; max-height: 300px; overflow-y: auto;">
+            '''
+            for ref_detail in reference_details:
+                status_color = {"complete": "#27ae60", "incomplete": "#f39c12", "not_found": "#e74c3c"}.get(ref_detail.get("completion_status", "unknown"), "#95a5a6")
+                html += f'''
+                        <div style="padding: 10px; margin: 5px 0; border-left: 4px solid {status_color}; background: #f8f9fa;">
+                            <div style="font-weight: bold; margin-bottom: 5px;">Reference {ref_detail.get('reference_id', '')}</div>
+                            <div style="margin-bottom: 5px;">{ref_detail.get('formatted_citation', '')}</div>
+                            <small style="color: #666;">Type: {ref_detail.get('source_type', 'unknown').title()} | Status: {ref_detail.get('completion_status', 'unknown').title()}</small>
+                        </div>
+                '''
+            html += '''
+                    </div>
+                </details>
+            </div>
+            '''
+        html += '''
+        </div>
+        '''
+    # SUMMARY STATISTICS SECTION (Secondary Information)
+    if summary_stats:
+        html += f'''
+        <div style="margin-bottom: 20px;">
+            <h5>📊 Summary Statistics</h5>
+        '''
+        # Document metadata
+        if "document_metadata" in summary_stats:
+            metadata = summary_stats["document_metadata"]
+            html += f'''
+            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 15px; margin-bottom: 20px;">
+                <div style="background: white; padding: 15px; border-radius: 8px; border: 1px solid #e1e5e9; text-align: center;">
+                    <div style="font-size: 20px; font-weight: bold; color: #667eea;">{metadata.get('product', 'Not detected')}</div>
+                    <div style="font-size: 12px; color: #666; margin-top: 5px;">Product</div>
+                </div>
+                <div style="background: white; padding: 15px; border-radius: 8px; border: 1px solid #e1e5e9; text-align: center;">
+                    <div style="font-size: 20px; font-weight: bold; color: #667eea;">{metadata.get('language', 'Not detected')}</div>
+                    <div style="font-size: 12px; color: #666; margin-top: 5px;">Language</div>
+                </div>
+                <div style="background: white; padding: 15px; border-radius: 8px; border: 1px solid #e1e5e9; text-align: center;">
+                    <div style="font-size: 20px; font-weight: bold; color: #667eea;">{', '.join(metadata.get('countries', [])) if metadata.get('countries') else 'Not detected'}</div>
+                    <div style="font-size: 12px; color: #666; margin-top: 5px;">Countries</div>
+                </div>
+            </div>
+            '''
+        # Claims analysis summary
+        if "claims_analysis" in summary_stats:
+            claims = summary_stats["claims_analysis"]
+            html += f'''
+            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 15px; margin-bottom: 20px;">
+                <div style="background: white; padding: 15px; border-radius: 8px; border: 1px solid #e1e5e9; text-align: center;">
+                    <div style="font-size: 20px; font-weight: bold; color: #667eea;">{claims.get('total_claims', 0)}</div>
+                    <div style="font-size: 12px; color: #666; margin-top: 5px;">Total Claims</div>
+                </div>
+                <div style="background: white; padding: 15px; border-radius: 8px; border: 1px solid #e1e5e9; text-align: center;">
+                    <div style="font-size: 20px; font-weight: bold; color: #667eea;">{claims.get('core_claims_count', 0)}</div>
+                    <div style="font-size: 12px; color: #666; margin-top: 5px;">Core Claims</div>
+                </div>
+            </div>
+            '''
+        # Claims anchoring summary
+        if "claims_anchoring" in summary_stats and "summary" in summary_stats["claims_anchoring"]:
+            summary = summary_stats["claims_anchoring"]["summary"]
+            validation_rate = int((summary.get("validation_rate", 0) * 100))
+            html += f'''
+            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 15px; margin-bottom: 20px;">
+                <div style="background: white; padding: 15px; border-radius: 8px; border: 1px solid #e1e5e9; text-align: center;">
+                    <div style="font-size: 20px; font-weight: bold; color: #667eea;">{summary.get('total_claims_processed', 0)}</div>
+                    <div style="font-size: 12px; color: #666; margin-top: 5px;">Claims Processed</div>
+                </div>
+                <div style="background: white; padding: 15px; border-radius: 8px; border: 1px solid #e1e5e9; text-align: center;">
+                    <div style="font-size: 20px; font-weight: bold; color: #667eea;">{summary.get('successfully_validated', 0)}</div>
+                    <div style="font-size: 12px; color: #666; margin-top: 5px;">Successfully Validated</div>
+                </div>
+                <div style="background: white; padding: 15px; border-radius: 8px; border: 1px solid #e1e5e9; text-align: center;">
+                    <div style="font-size: 20px; font-weight: bold; color: #667eea;">{validation_rate}%</div>
+                    <div style="font-size: 12px; color: #666; margin-top: 5px;">Validation Rate</div>
+                </div>
+            </div>
+            '''
+        # Reference formatting summary
+        if "reference_formatting" in summary_stats:
+            refs = summary_stats["reference_formatting"]
+            html += f'''
+            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 15px;">
+                <div style="background: white; padding: 15px; border-radius: 8px; border: 1px solid #e1e5e9; text-align: center;">
+                    <div style="font-size: 20px; font-weight: bold; color: #667eea;">{refs.get('total_references', 0)}</div>
+                    <div style="font-size: 12px; color: #666; margin-top: 5px;">References Formatted</div>
+                </div>
+            </div>
+            '''
+        html += '''
+        </div>
+        '''
+    return html
+def create_openai_client():
+    """Create and return OpenAI client instance."""
+    geai_api_key = os.getenv("GEAI_API_KEY")
+    geai_base_url = os.getenv("GEAI_API_BASE_URL")
+    return OpenAI(api_key=geai_api_key, base_url=geai_base_url)
 def answer_question(question: str):
     """
     Función para responder preguntas usando OpenAI LLM
         raise HTTPException(status_code=400, detail="Please provide a question.")
     # Obtener API key de OpenAI desde variables de entorno
+    geai_api_key = os.getenv("GEAI_API_KEY")
+    geai_base_url = os.getenv("GEAI_API_BASE_URL")
+    if not geai_api_key:
+        raise HTTPException(status_code=500, detail="GEAI API key not configured")
+    # Inicializar OpenAI client
     try:
+        # Create OpenAI client
+        client = create_openai_client()
+        # Make the LLM call
+        completion = client.chat.completions.create(
+            model="openai/gpt-4o-mini",
+            messages=[{"role": "user", "content": f"Answer the following question clearly and concisely: {question}"}],
+            temperature=0.7,
+            max_tokens=500
         )
+        response = completion.choices[0].message.content
         return GenerateResponse(text=response.strip())
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Error generating response: {str(e)}")
+def get_base_html(active_section: str = "ai-generator", question_value: str = "", document_value: str = "", ai_response: str = "", research_response: str = ""):
+    """Generate base HTML with optional responses"""
+    ai_display = "" if active_section == "ai-generator" else "display: none;"
+    research_display = "" if active_section == "research-team" else "display: none;"
+    return f"""
     <!DOCTYPE html>
     <html>
     <head>
         <title>SciResearch API</title>
+        <meta name="viewport" content="width=device-width, initial-scale=1.0">
         <style>
+            * {{ margin: 0; padding: 0; box-sizing: border-box; }}
+            body {{
+                font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+                background-color: #f5f7fa;
+                color: #333;
+            }}
+            .app-container {{
+                display: flex;
+                min-height: 100vh;
+            }}
+            /* Sidebar Styles */
+            .sidebar {{
+                width: 300px;
+                background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+                color: white;
+                padding: 20px;
+                box-shadow: 2px 0 10px rgba(0,0,0,0.1);
+                position: fixed;
+                height: 100vh;
+                overflow-y: auto;
+            }}
+            .sidebar h1 {{
+                font-size: 24px;
+                margin-bottom: 10px;
+                display: flex;
+                align-items: center;
+                gap: 10px;
+            }}
+            .sidebar p {{
+                margin-bottom: 30px;
+                opacity: 0.9;
+                font-size: 14px;
+            }}
+            .sidebar-section {{
+                margin-bottom: 30px;
+            }}
+            .sidebar-section h3 {{
+                font-size: 16px;
+                margin-bottom: 15px;
+                border-bottom: 1px solid rgba(255,255,255,0.3);
+                padding-bottom: 5px;
+            }}
+            .nav-link {{
+                display: block;
+                color: rgba(255,255,255,0.8);
+                text-decoration: none;
+                padding: 10px 15px;
+                margin: 5px 0;
+                border-radius: 8px;
+                transition: background 0.3s;
+            }}
+            .nav-link:hover, .nav-link.active {{
+                background: rgba(255,255,255,0.2);
+                color: white;
+            }}
+            /* Main Content Styles */
+            .main-content {{
+                flex: 1;
+                margin-left: 300px;
+                padding: 40px;
+                background: white;
+                min-height: 100vh;
+            }}
+            .content-header {{
+                margin-bottom: 30px;
+            }}
+            .content-header h2 {{
+                font-size: 28px;
+                color: #333;
+                margin-bottom: 10px;
+            }}
+            .content-header p {{
+                color: #666;
+                font-size: 16px;
+            }}
+            .generator-section {{
+                background: white;
+                border-radius: 12px;
+                padding: 30px;
+                box-shadow: 0 2px 20px rgba(0,0,0,0.08);
+                margin-bottom: 20px;
+            }}
+            .form-group {{
+                margin-bottom: 20px;
+            }}
+            .form-group label {{
+                display: block;
+                font-weight: 600;
+                margin-bottom: 8px;
+                color: #333;
+            }}
+            input[type="text"], textarea {{
+                width: 100%;
+                padding: 12px 16px;
+                border: 2px solid #e1e5e9;
+                border-radius: 8px;
+                font-size: 14px;
+                transition: border-color 0.3s, box-shadow 0.3s;
+                font-family: inherit;
+            }}
+            input[type="text"]:focus, textarea:focus {{
+                outline: none;
+                border-color: #667eea;
+                box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1);
+            }}
+            textarea {{
+                height: 200px;
+                resize: vertical;
+            }}
+            .btn {{
+                padding: 12px 24px;
+                border: none;
+                border-radius: 8px;
+                cursor: pointer;
+                font-size: 14px;
+                font-weight: 600;
+                transition: all 0.3s;
+                display: inline-flex;
+                align-items: center;
+                gap: 8px;
+            }}
+            .btn-primary {{
+                background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+                color: white;
+            }}
+            .btn-primary:hover {{
+                transform: translateY(-2px);
+                box-shadow: 0 4px 15px rgba(102, 126, 234, 0.4);
+            }}
+            .btn-secondary {{
+                background: linear-gradient(135deg, #36d1dc 0%, #5b86e5 100%);
+                color: white;
+            }}
+            .btn-secondary:hover {{
+                transform: translateY(-2px);
+                box-shadow: 0 4px 15px rgba(54, 209, 220, 0.4);
+            }}
+            .response-section {{
+                margin-top: 25px;
+                padding: 20px;
+                background: #f8f9fa;
+                border-radius: 8px;
+                border-left: 4px solid #667eea;
+            }}
+            .response-section.error {{
+                border-left-color: #dc3545;
+            }}
+            .response-section h4 {{
+                margin-bottom: 15px;
+                color: #333;
+            }}
+            .api-info {{
+                background: #f8f9fa;
+                padding: 20px;
+                border-radius: 8px;
+                margin-top: 30px;
+            }}
+            .api-info h3 {{
+                margin-bottom: 15px;
+                color: #333;
+            }}
+            .api-info ul {{
+                list-style: none;
+            }}
+            .api-info li {{
+                padding: 8px 0;
+                border-bottom: 1px solid #e1e5e9;
+            }}
+            .api-info li:last-child {{
+                border-bottom: none;
+            }}
+            .api-info a {{
+                color: #667eea;
+                text-decoration: none;
+            }}
+            .api-info a:hover {{
+                text-decoration: underline;
+            }}
+            /* Responsive Design */
+            @media (max-width: 768px) {{
+                .sidebar {{
+                    width: 100%;
+                    position: relative;
+                    height: auto;
+                }}
+                .main-content {{
+                    margin-left: 0;
+                    padding: 20px;
+                }}
+                .app-container {{
+                    flex-direction: column;
+                }}
+            }}
         </style>
     </head>
     <body>
+        <div class="app-container">
+            <!-- Sidebar -->
+            <div class="sidebar">
+                <h1>🦀 SciResearch</h1>
+                <p>Scientific Research FastAPI application with AI integration and Research Team</p>
+                <div class="sidebar-section">
+                    <h3>📡 Select Generator</h3>
+                    <a href="/" class="nav-link {'active' if active_section == 'ai-generator' else ''}">💬 AI Question Generator</a>
+                    <a href="/?mode=research" class="nav-link {'active' if active_section == 'research-team' else ''}">📄 Research Team Processor</a>
                 </div>
+                <div class="sidebar-section">
+                    <h3>📋 Quick Links</h3>
+                    <a href="/docs" class="nav-link">📚 API Documentation</a>
+                    <a href="/api/health" class="nav-link">🔧 Health Endpoint</a>
                 </div>
             </div>
+            <!-- Main Content -->
+            <div class="main-content">
+                <!-- AI Generator Section -->
+                <div id="ai-generator-section" class="generator-section" style="{ai_display}">
+                    <div class="content-header">
+                        <h2>💬 AI Question Generator</h2>
+                        <p>Ask questions and get AI-powered responses from the research assistant</p>
+                    </div>
+                    <form action="/ask" method="post">
+                        <div class="form-group">
+                            <label for="question">Enter your question:</label>
+                            <input type="text" name="question" id="question"
+                                   value="{question_value}"
+                                   placeholder="What would you like to know about scientific research?"
+                                   required />
+                        </div>
+                        <button type="submit" class="btn btn-primary">
+                            🚀 Submit Question
+                        </button>
+                    </form>
+                    {ai_response}
+                </div>
+                <!-- Research Team Section -->
+                <div id="research-team-section" class="generator-section" style="{research_display}">
+                    <div class="content-header">
+                        <h2>📄 Research Team Document Processor</h2>
+                        <p>Process documents for claims anchoring and reference formatting using the AI research team</p>
+                    </div>
+                    <form action="/process" method="post">
+                        <div class="form-group">
+                            <label for="document">Paste your document content:</label>
+                            <textarea name="document_content" id="document"
+                                      placeholder="Paste the content of your research document here..."
+                                      required>{document_value}</textarea>
+                        </div>
+                        <button type="submit" class="btn btn-secondary">
+                            🔬 Process Document
+                        </button>
+                    </form>
+                    {research_response}
                 </div>
+                <!-- API Information -->
+                <div class="api-info">
+                    <h3>🔗 Available API Endpoints</h3>
+                    <ul>
+                        <li><strong>GET /</strong> - This HTML interface</li>
+                        <li><strong>GET /docs</strong> - Interactive API documentation</li>
+                        <li><strong>GET /api/hello</strong> - JSON greeting message</li>
+                        <li><strong>GET /api/health</strong> - Application health check</li>
+                        <li><strong>POST /ask</strong> - AI question answering (form)</li>
+                        <li><strong>POST /process</strong> - Document processing (form)</li>
+                        <li><strong>POST /api/generate</strong> - AI question answering (JSON API)</li>
+                        <li><strong>POST /api/research/process</strong> - Document processing (JSON API)</li>
+                    </ul>
                 </div>
             </div>
         </div>
     </body>
     </html>
     """
+@app.get("/", response_class=HTMLResponse)
+def read_root(mode: str = None):
+    """
+    Main HTML interface - supports switching between AI generator and research team
+    """
+    if mode == "research":
+        return get_base_html("research-team")
+    else:
+        return get_base_html("ai-generator")
 @app.get("/api/hello")
 def greet_json():
     """
     Endpoint para verificar el estado de la aplicación
     """
+    geai_configured = bool(os.getenv("GEAI_API_KEY")) and bool(os.getenv("GEAI_API_BASE_URL"))
     return {
         "status": "healthy",
         "service": "sciresearch",
         "version": "1.0.0",
+        "geai_configured": geai_configured,
         "research_team_available": True
     }
     """
     return answer_question(question=request.question)
+@app.post("/ask", response_class=HTMLResponse)
+def ask_question_form(question: str = Form(...)):
+    """
+    Form submission endpoint for questions - returns HTML response
+    """
+    try:
+        result = answer_question(question)
+        answer_text = result.text
+        status = "success"
+        error_msg = None
+    except Exception as e:
+        answer_text = ""
+        status = "error"
+        error_msg = str(e)
+    return get_html_with_response(question, answer_text, status, error_msg)
+@app.post("/process", response_class=HTMLResponse)
+def process_document_form(document_content: str = Form(...)):
+    """
+    Form submission endpoint for document processing - returns HTML response
+    """
+    try:
+        team = get_research_team()
+        import asyncio
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        result = loop.run_until_complete(team.process_document(document_content))
+        status = "success"
+        error_msg = None
+    except Exception as e:
+        result = {}
+        status = "error"
+        error_msg = str(e)
+    return get_html_with_research_response(document_content, result, status, error_msg)
 @app.post("/api/research/process", summary="Process document with Research Team", tags=["Research Team"], response_model=ResearchResponse)
 async def process_document_research(request: DocumentRequest):
     """

research_team.py CHANGED Viewed

@@ -13,12 +13,11 @@ from enum import Enum
 import operator
 from datetime import datetime
-from langchain_core.messages import HumanMessage, AIMessage
-from langchain_openai import ChatOpenAI
-from langchain_core.prompts import ChatPromptTemplate
 from langgraph.graph import StateGraph, START, END
 from langgraph.graph.message import add_messages
-from langgraph.prebuilt import ToolNode, create_react_agent
 from langchain_core.tools import tool
 from pydantic import BaseModel
 import re
@@ -96,24 +95,21 @@ class ResearchTeamState(TypedDict):
 # Web Search Tools Implementation
 class WebSearchManager:
-    """Manager for web search operations using OpenAI agent with web_search_preview"""
     def __init__(self):
-        """Initialize the web search agent"""
         try:
-            self.agent = create_react_agent(
-                model="openai:gpt-4o-mini",
-                tools=[{"type": "web_search_preview"}]
-            )
-            logger.info("✅ Web search agent initialized successfully")
         except Exception as e:
-            logger.error(f"❌ Failed to initialize web search agent: {e}")
-            self.agent = None
     def search_web_sync(self, query: str, source_hint: str = "") -> str:
         """Execute web search synchronously with robust error handling"""
-        if not self.agent:
-            logger.error("Web search agent not available")
             return ""
         try:
@@ -122,20 +118,19 @@ class WebSearchManager:
             logger.info(f"🔍 Executing web search: '{enhanced_query[:50]}...'")
-            # Use synchronous invoke instead of async
-            response = self.agent.invoke({
-                "messages": [enhanced_query]
-            })
-            # Extract content from response
-            if hasattr(response, 'messages') and response.messages:
-                content = response.messages[-1].content
-                logger.info(f"✅ Search response received: {len(content)} characters")
-                return content
-            else:
-                content = str(response)
-                logger.info(f"✅ Search response (str): {len(content)} characters")
-                return content
         except Exception as e:
             logger.error(f"❌ Web search error: {e}")
@@ -198,6 +193,12 @@ def get_web_search_manager():
         web_search_manager = WebSearchManager()
     return web_search_manager
 @tool
 def google_scholar_search(query: str, claim_id: str) -> Dict[str, Any]:
     """Real Google Scholar search using web search agent"""
@@ -333,7 +334,7 @@ class AnalyzerAgent:
     def __init__(self, llm):
         self.llm = llm
-        self.prompt = ChatPromptTemplate.from_template("""
         You are an AI assistant specialized in analyzing content and extracting claims systematically.
         GUIDELINES:
@@ -351,25 +352,22 @@ class AnalyzerAgent:
         RESPONSE FORMAT:
         Provide response in JSON format with:
-        {{
             "product": "product_name_lowercase",
             "countries": ["country1", "country2"],
             "language": "detected_language",
             "claims": [
-                {{
                     "id": "claim_1",
                     "text": "exact claim text",
                     "type": "core|supporting|contextual",
                     "importance_score": 9,
                     "position": 1,
                     "context": "surrounding context"
-                }}
             ]
-        }}
-        Document Content:
-        {document_content}
-        """)
     async def analyze(self, document_content: str) -> Dict[str, Any]:
         """Analyze document and extract structured claims"""
@@ -377,12 +375,22 @@ class AnalyzerAgent:
         try:
             logger.info("Processing document content for claims extraction")
-            response = await self.llm.ainvoke(
-                self.prompt.format_messages(document_content=document_content)
             )
             # Parse JSON response
-            result = json.loads(response.content)
             # Separate core claims for priority processing
             core_claims = [claim for claim in result["claims"] if claim["type"] == "core"]
@@ -466,7 +474,7 @@ class ResearcherAgent:
     def __init__(self, llm):
         self.llm = llm
-        self.prompt = ChatPromptTemplate.from_template("""
         You are an AI assistant specialized in claims anchoring and reference validation.
         GUIDELINES:
@@ -480,24 +488,21 @@ class ResearcherAgent:
            - Rate the relevance and quality of support
         RESPONSE FORMAT:
-        {{
-            "claim_id": "{claim_id}",
             "validation_status": "validated|partial|unsupported",
             "anchored_references": [
-                {{
                     "reference_id": "ref_id",
                     "supporting_text": "exact text that supports claim",
                     "relevance_score": 0.92,
                     "section": "Results"
-                }}
             ],
             "supporting_passages": ["passage1", "passage2"],
             "quality_assessment": "assessment text"
-        }}
-        Claim: {claim_text}
-        Search Results: {search_results}
-        """)
     async def anchor_claim(self, claim: Dict[str, Any], search_results: List[Dict]) -> Dict[str, Any]:
         """Perform claims anchoring for a specific claim"""
@@ -514,15 +519,19 @@ class ResearcherAgent:
             logger.debug(f"Retrieved full content for {len(enriched_results)} top references")
-            response = await self.llm.ainvoke(
-                self.prompt.format_messages(
-                    claim_id=claim["id"],
-                    claim_text=claim["text"],
-                    search_results=json.dumps(enriched_results, indent=2)
-                )
             )
-            result = json.loads(response.content)
             result["claim_text"] = claim["text"]
             logger.info(f"Claim {claim_id} anchored: {result.get('validation_status', 'unknown')} status")
@@ -544,7 +553,7 @@ class EditorAgent:
     def __init__(self, llm):
         self.llm = llm
-        self.prompt = ChatPromptTemplate.from_template("""
         You are an expert in reference formatting using J&J formatting guidelines.
         GUIDELINES:
@@ -556,29 +565,24 @@ class EditorAgent:
         2. Special rules:
            - Use first, second, third authors + "et al." when more than 3 authors
            - Use italic format ONLY for book titles
-           - Translate terms based on content language: {language}
         3. Complete missing information where possible
         4. Maintain original reference order
         RESPONSE FORMAT:
-        {{
             "formatted_references": [
-                {{
                     "id": "ref_id",
                     "original": "original reference text",
                     "formatted": "properly formatted reference",
                     "changes_applied": "description of changes",
                     "source_type": "journal|book|website|etc",
                     "completion_status": "complete|incomplete|not_found"
-                }}
             ]
-        }}
-        References to format:
-        {references}
-        Content Language: {language}
-        """)
     async def format_references(self, references: List[Dict], language: str = "english") -> Dict[str, Any]:
         """Format references according to J&J guidelines"""
@@ -586,14 +590,20 @@ class EditorAgent:
         logger.info(f"Content language: {language}")
         try:
-            response = await self.llm.ainvoke(
-                self.prompt.format_messages(
-                    references=json.dumps(references, indent=2),
-                    language=language
-                )
             )
-            result = json.loads(response.content)
             formatted_count = len(result.get("formatted_references", []))
             logger.info(f"Reference formatting complete: {formatted_count} references processed")
@@ -610,11 +620,7 @@ class ResearchTeamWorkflow:
         logger.info("Initializing Research Team Workflow")
         # Initialize LLM
-        self.llm = ChatOpenAI(
-            model="gpt-4",
-            temperature=0.1,
-            api_key=os.getenv("OPENAI_API_KEY")
-        )
         # Initialize agents
         self.analyzer = AnalyzerAgent(self.llm)
@@ -782,38 +788,76 @@ class ResearchTeamWorkflow:
         """Assemble final results"""
         logger.info("STEP 6: Final Assembly - Generating comprehensive report")
         final_output = {
-            "document_metadata": {
-                "product": state["product"],
-                "countries": state["countries"],
-                "language": state["language"]
-            },
-            "claims_analysis": {
-                "total_claims": len(state["all_claims"]),
-                "core_claims_count": len(state["core_claims"]),
-                "claims_details": state["all_claims"]
-            },
-            "claims_anchoring": {
-                "results": state["anchoring_results"],
-                "summary": self._generate_anchoring_summary(state["anchoring_results"])
-            },
-            "reference_formatting": {
-                "formatted_references": state["formatted_references"],
-                "total_references": len(state["formatted_references"])
             },
-            "processing_status": state.get("processing_status", {})
         }
         state["final_output"] = final_output
         # Log final summary
-        summary = final_output["claims_anchoring"]["summary"]
         logger.info("FINAL RESULTS SUMMARY:")
-        logger.info(f"   Total claims processed: {final_output['claims_analysis']['total_claims']}")
-        logger.info(f"   Core claims: {final_output['claims_analysis']['core_claims_count']}")
-        logger.info(f"   Successfully validated: {summary['successfully_validated']}")
-        logger.info(f"   Validation rate: {summary['validation_rate']:.1%}")
-        logger.info(f"   References formatted: {final_output['reference_formatting']['total_references']}")
         logger.info("STEP 6 COMPLETE: Research Team workflow finished successfully!")
         return state

 import operator
 from datetime import datetime
+# Use OpenAI directly like in app.py
+from openai import OpenAI
 from langgraph.graph import StateGraph, START, END
 from langgraph.graph.message import add_messages
 from langchain_core.tools import tool
 from pydantic import BaseModel
 import re
 # Web Search Tools Implementation
 class WebSearchManager:
+    """Manager for web search operations using OpenAI with gpt-4o-search-preview"""
     def __init__(self):
+        """Initialize the web search client"""
         try:
+            self.client = create_openai_client()
+            logger.info("✅ Web search client initialized successfully")
         except Exception as e:
+            logger.error(f"❌ Failed to initialize web search client: {e}")
+            self.client = None
     def search_web_sync(self, query: str, source_hint: str = "") -> str:
         """Execute web search synchronously with robust error handling"""
+        if not self.client:
+            logger.error("Web search client not available")
             return ""
         try:
             logger.info(f"🔍 Executing web search: '{enhanced_query[:50]}...'")
+            # Use OpenAI client with regular model for web search
+            completion = self.client.chat.completions.create(
+                model="openai/gpt-4o-mini-search-preview",
+                messages=[
+                    {"role": "system", "content": "You are a web search assistant. Provide comprehensive and accurate information based on the search query. Include relevant details, sources, and context."},
+                    {"role": "user", "content": enhanced_query}
+                ],
+                max_tokens=2000
+            )
+            content = completion.choices[0].message.content
+            logger.info(f"✅ Search response received: {len(content)} characters")
+            return content
         except Exception as e:
             logger.error(f"❌ Web search error: {e}")
         web_search_manager = WebSearchManager()
     return web_search_manager
+def create_openai_client():
+    """Create and return OpenAI client instance using same config as app.py"""
+    geai_api_key = os.getenv("GEAI_API_KEY")
+    geai_base_url = os.getenv("GEAI_API_BASE_URL")
+    return OpenAI(api_key=geai_api_key, base_url=geai_base_url)
 @tool
 def google_scholar_search(query: str, claim_id: str) -> Dict[str, Any]:
     """Real Google Scholar search using web search agent"""
     def __init__(self, llm):
         self.llm = llm
+        self.system_prompt = """
         You are an AI assistant specialized in analyzing content and extracting claims systematically.
         GUIDELINES:
         RESPONSE FORMAT:
         Provide response in JSON format with:
+        {
             "product": "product_name_lowercase",
             "countries": ["country1", "country2"],
             "language": "detected_language",
             "claims": [
+                {
                     "id": "claim_1",
                     "text": "exact claim text",
                     "type": "core|supporting|contextual",
                     "importance_score": 9,
                     "position": 1,
                     "context": "surrounding context"
+                }
             ]
+        }
+        """
     async def analyze(self, document_content: str) -> Dict[str, Any]:
         """Analyze document and extract structured claims"""
         try:
             logger.info("Processing document content for claims extraction")
+            # Use direct OpenAI client like in app.py (synchronous call)
+            completion = self.llm.chat.completions.create(
+                model="openai/gpt-4o-mini",
+                messages=[
+                    {"role": "system", "content": self.system_prompt},
+                    {"role": "user", "content": f"Document Content:\n{document_content}"}
+                ],
+                temperature=0.1,
+                max_tokens=2000
             )
+            response_content = completion.choices[0].message.content
             # Parse JSON response
+            result = json.loads(response_content)
             # Separate core claims for priority processing
             core_claims = [claim for claim in result["claims"] if claim["type"] == "core"]
     def __init__(self, llm):
         self.llm = llm
+        self.system_prompt = """
         You are an AI assistant specialized in claims anchoring and reference validation.
         GUIDELINES:
            - Rate the relevance and quality of support
         RESPONSE FORMAT:
+        {
+            "claim_id": "claim_id_value",
             "validation_status": "validated|partial|unsupported",
             "anchored_references": [
+                {
                     "reference_id": "ref_id",
                     "supporting_text": "exact text that supports claim",
                     "relevance_score": 0.92,
                     "section": "Results"
+                }
             ],
             "supporting_passages": ["passage1", "passage2"],
             "quality_assessment": "assessment text"
+        }
+        """
     async def anchor_claim(self, claim: Dict[str, Any], search_results: List[Dict]) -> Dict[str, Any]:
         """Perform claims anchoring for a specific claim"""
             logger.debug(f"Retrieved full content for {len(enriched_results)} top references")
+            # Use direct OpenAI client like in app.py
+            completion = self.llm.chat.completions.create(
+                model="openai/gpt-4o-mini",
+                messages=[
+                    {"role": "system", "content": self.system_prompt},
+                    {"role": "user", "content": f"Claim: {claim['text']}\nSearch Results: {json.dumps(enriched_results, indent=2)}"}
+                ],
+                temperature=0.1,
+                max_tokens=1500
             )
+            response_content = completion.choices[0].message.content
+            result = json.loads(response_content)
             result["claim_text"] = claim["text"]
             logger.info(f"Claim {claim_id} anchored: {result.get('validation_status', 'unknown')} status")
     def __init__(self, llm):
         self.llm = llm
+        self.system_prompt = """
         You are an expert in reference formatting using J&J formatting guidelines.
         GUIDELINES:
         2. Special rules:
            - Use first, second, third authors + "et al." when more than 3 authors
            - Use italic format ONLY for book titles
+           - Translate terms based on content language
         3. Complete missing information where possible
         4. Maintain original reference order
         RESPONSE FORMAT:
+        {
             "formatted_references": [
+                {
                     "id": "ref_id",
                     "original": "original reference text",
                     "formatted": "properly formatted reference",
                     "changes_applied": "description of changes",
                     "source_type": "journal|book|website|etc",
                     "completion_status": "complete|incomplete|not_found"
+                }
             ]
+        }
+        """
     async def format_references(self, references: List[Dict], language: str = "english") -> Dict[str, Any]:
         """Format references according to J&J guidelines"""
         logger.info(f"Content language: {language}")
         try:
+            # Use direct OpenAI client like in app.py
+            completion = self.llm.chat.completions.create(
+                model="openai/gpt-4o-mini",
+                messages=[
+                    {"role": "system", "content": self.system_prompt},
+                    {"role": "user", "content": f"References to format:\n{json.dumps(references, indent=2)}\n\nContent Language: {language}"}
+                ],
+                temperature=0.1,
+                max_tokens=2000
             )
+            response_content = completion.choices[0].message.content
+            result = json.loads(response_content)
             formatted_count = len(result.get("formatted_references", []))
             logger.info(f"Reference formatting complete: {formatted_count} references processed")
         logger.info("Initializing Research Team Workflow")
         # Initialize LLM
+        self.llm = create_openai_client()
         # Initialize agents
         self.analyzer = AnalyzerAgent(self.llm)
         """Assemble final results"""
         logger.info("STEP 6: Final Assembly - Generating comprehensive report")
+        # Generate anchoring summary for statistics
+        anchoring_summary = self._generate_anchoring_summary(state["anchoring_results"])
         final_output = {
+            # DETAILED CONTENT ANALYSIS (Priority Content)
+            "detailed_analysis": {
+                "claims_extracted": {
+                    "all_claims": state["all_claims"],
+                    "core_claims": state["core_claims"],
+                    "total_claims_found": len(state["all_claims"]),
+                    "core_claims_count": len(state["core_claims"])
+                },
+                "anchoring_results": {
+                    "detailed_anchoring": state["anchoring_results"],
+                    "claims_with_evidence": [
+                        {
+                            "claim_id": result["claim_id"],
+                            "claim_text": result["claim_text"],
+                            "validation_status": result.get("validation_status", "unknown"),
+                            "supporting_evidence": result.get("supporting_passages", []),
+                            "anchored_references": result.get("anchored_references", []),
+                            "quality_assessment": result.get("quality_assessment", "")
+                        }
+                        for result in state["anchoring_results"]
+                    ]
+                },
+                "formatted_references": {
+                    "references": state["formatted_references"],
+                    "reference_details": [
+                        {
+                            "reference_id": ref.get("id", ""),
+                            "formatted_citation": ref.get("formatted", ""),
+                            "source_type": ref.get("source_type", ""),
+                            "completion_status": ref.get("completion_status", "")
+                        }
+                        for ref in state["formatted_references"]
+                    ]
+                }
             },
+            # SUMMARY STATISTICS (Secondary Information)
+            "summary_statistics": {
+                "document_metadata": {
+                    "product": state["product"],
+                    "countries": state["countries"],
+                    "language": state["language"]
+                },
+                "claims_analysis": {
+                    "total_claims": len(state["all_claims"]),
+                    "core_claims_count": len(state["core_claims"])
+                },
+                "claims_anchoring": {
+                    "summary": anchoring_summary
+                },
+                "reference_formatting": {
+                    "total_references": len(state["formatted_references"])
+                },
+                "processing_status": state.get("processing_status", {})
+            }
         }
         state["final_output"] = final_output
         # Log final summary
         logger.info("FINAL RESULTS SUMMARY:")
+        logger.info(f"   Total claims processed: {final_output['summary_statistics']['claims_analysis']['total_claims']}")
+        logger.info(f"   Core claims: {final_output['summary_statistics']['claims_analysis']['core_claims_count']}")
+        logger.info(f"   Successfully validated: {anchoring_summary['successfully_validated']}")
+        logger.info(f"   Validation rate: {anchoring_summary['validation_rate']:.1%}")
+        logger.info(f"   References formatted: {final_output['summary_statistics']['reference_formatting']['total_references']}")
         logger.info("STEP 6 COMPLETE: Research Team workflow finished successfully!")
         return state