updating model to mistral latest

README.md

@@ -67,7 +67,7 @@ tag: agent-demo-track
 ### 1. Prerequisites
 
 - Python 3.8+
-- Google Gemini API key
+- Mistral API key
 
 ### 2. Installation
 
@@ -85,7 +85,7 @@ pip install -r requirements.txt
 Create a `.env` file in the project root:
 
 ```bash
-GEMINI_API_KEY=your_gemini_api_key_here
+Mistral_API_KEY=your_Mistral_api_key_here
 ```
 
 ### 4. Launch the Application
@@ -170,7 +170,7 @@ After the initial analysis is complete:
 
 - **Frontend**: Gradio with custom CSS and real-time progress
 - **AI Agents**: 
-  - Primary SmolAgents powered by Gemini 2.5 Flash
+  - Primary SmolAgents powered by Mistral Medium Latest
   - Specialized follow-up agent for interactive analysis ⭐
 - **Search**: BM25 keyword matching with TF-IDF preprocessing
 - **Translation**: LLM-powered bilingual query translation

agent.py

@@ -26,7 +26,7 @@ def create_web_agent(step_callback):
     search_tool = DuckDuckGoSearchTool()
     model = LiteLLMModel(
                 #model_id="gemini/gemini-2.5-flash-preview-05-20",
-                model="mistralai/mistral-medium-latest",
+                model_id="mistral/mistral-medium-latest",
                 api_key=os.getenv("MISTRAL_API_KEY"),
             )
     web_agent = CodeAgent(
@@ -68,9 +68,14 @@ def generate_prompt(user_query=None, initial_search_results=None):
        - Get dataset description using get_dataset_description
     
     3. **Visualization Creation**:
-       - If geographic data (departments/regions) is available, create a map of France
-       - Create 3 additional non-map visualizations
-       - Save all visualizations as PNG files
+       - **French Map Creation**: If you have data by French departments or regions, use the plot_departments_data tool:
+         * Call: plot_departments_data(data_dict, title, filename, color_scheme='viridis')
+         * data_dict format: {"department_name": value, "department_code": value}
+         * Supports both department names (e.g., "Paris", "Bouches-du-Rhône") and codes (e.g., "75", "13")
+         * Example: plot_departments_data({"Paris": 2161, "Lyon": 513}, "Population by Department", "population_map.png")
+         * The tool automatically saves the map as PNG in generated_data folder
+       - Create 3 additional non-map visualizations using matplotlib/seaborn
+       - Save all visualizations as PNG files in generated_data folder
     
     4. **Report Generation**:
        - Write insightful analysis text for each visualization

app.py

@@ -33,7 +33,7 @@ def initialize_models():
         try:
             model = LiteLLMModel(
                 #model_id="gemini/gemini-2.5-flash-preview-05-20",
-                model="mistralai/mistral-medium-latest",
+                model_id="mistral/mistral-medium-latest",
                 api_key=os.getenv("MISTRAL_API_KEY")
             )
             llm_translator = CodeAgent(tools=[], model=model, max_steps=1)

followup_agent.py

@@ -23,7 +23,7 @@ def create_followup_agent():
     search_tool = DuckDuckGoSearchTool()
     model = LiteLLMModel(
         #model_id="gemini/gemini-2.5-flash-preview-05-20",
-        model="mistralai/mistral-medium-latest",
+        model_id="mistral/mistral-medium-latest",
         api_key=os.getenv("MISTRAL_API_KEY"),
     )
     

tools/retrieval_tools.py

@@ -33,8 +33,9 @@ def _initialize_retrieval_system():
     if _llm_translator is None:
         try:
             model = LiteLLMModel(
-                model_id="gemini/gemini-2.5-flash-preview-05-20",
-                api_key=os.getenv("GEMINI_API_KEY")
+                #model_id="gemini/gemini-2.5-flash-preview-05-20",
+                model_id="mistral/mistral-medium-latest",
+                api_key=os.getenv("MISTRAL_API_KEY")
             )
             _llm_translator = CodeAgent(tools=[], model=model, max_steps=1)
             print("✅ LLM translator initialized")