Create app.py

app.py

@@ -0,0 +1,78 @@
+import os
+import gradio as gr
+from langchain_core.prompts import PromptTemplate
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_google_genai import ChatGoogleGenerativeAI
+import google.generativeai as genai
+from langchain.chains.question_answering import load_qa_chain
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from langsmith import LangSmithClient  # Hypothetical import for LangSmith
+
+# Configure Gemini API
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+# Load Mistral model
+model_path = "nvidia/Mistral-NeMo-Minitron-8B-Base"
+mistral_tokenizer = AutoTokenizer.from_pretrained(model_path)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+dtype = torch.bfloat16
+mistral_model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device)
+
+# Initialize LangSmith client
+langsmith_client = LangSmithClient(api_key=os.getenv("LANGSMITH_API_KEY"))
+
+def initialize(file_path, question):
+    try:
+        model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+        prompt_template = """Answer the question as precise as possible using the provided context. If the answer is
+                              not contained in the context, say "answer not available in context" \n\n
+                              Context: \n {context}?\n
+                              Question: \n {question} \n
+                              Answer:
+                            """
+        prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+        
+        if os.path.exists(file_path):
+            pdf_loader = PyPDFLoader(file_path)
+            pages = pdf_loader.load_and_split()
+            context = "\n".join(str(page.page_content) for page in pages[:10])
+            stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+            stuff_answer = stuff_chain({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True)
+            gemini_answer = stuff_answer['output_text']
+            
+            # Use Mistral model for additional text generation
+            mistral_prompt = f"Based on this answer: {gemini_answer}\nGenerate a follow-up question:"
+            mistral_inputs = mistral_tokenizer.encode(mistral_prompt, return_tensors='pt').to(device)
+            with torch.no_grad():
+                mistral_outputs = mistral_model.generate(mistral_inputs, max_length=100)
+            mistral_output = mistral_tokenizer.decode(mistral_outputs[0], skip_special_tokens=True)
+            
+            combined_output = f"Gemini Answer: {gemini_answer}\n\nMistral Follow-up: {mistral_output}"
+
+            # Log and evaluate the results using LangSmith
+            evaluation = langsmith_client.evaluate(gemini_answer, mistral_output, question)
+            return combined_output + f"\n\nEvaluation: {evaluation}"
+        else:
+            return "Error: Unable to process the document. Please ensure the PDF file is valid."
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Define Gradio Interface
+input_file = gr.File(label="Upload PDF File")
+input_question = gr.Textbox(label="Ask about the document")
+output_text = gr.Textbox(label="Answer - Combined Gemini and Mistral")
+
+def pdf_qa(file, question):
+    if file is None:
+        return "Please upload a PDF file first."
+    return initialize(file.name, question)
+
+# Create Gradio Interface
+gr.Interface(
+    fn=pdf_qa,
+    inputs=[input_file, input_question],
+    outputs=output_text,
+    title="RAG Knowledge Retrieval using Gemini API and Mistral Model",
+    description="Upload a PDF file and ask questions about the content."
+).launch()