Claude updates stuff

app.py

@@ -63,22 +63,23 @@ def initialize_faiss_index(embeddings):
 # Handle natural language queries
 conversation_history = []
 
-def handle_query(query, faiss_index, embeddings_texts, model):
-    global conversation_history
+# Global variables
+lecture_notes = fetch_lecture_notes()
+model_architectures = fetch_model_architectures()
+all_texts = lecture_notes + [model_architectures]
+embedding_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
+embeddings = create_embeddings(all_texts, embedding_model)
+faiss_index = initialize_faiss_index(np.array(embeddings))
 
+def handle_query(query, faiss_index, embeddings_texts, model):
     query_embedding = model.encode([query]).astype('float32')
-
-    # Search FAISS index
-    _, indices = faiss_index.search(query_embedding, 3)  # Retrieve top 3 results
+    _, indices = faiss_index.search(query_embedding, 3)
     relevant_texts = [embeddings_texts[idx] for idx in indices[0]]
-
-    # Combine relevant texts and truncate if necessary
     combined_text = "\n".join([text for text, _ in relevant_texts])
-    max_length = 500  # Adjust as necessary
+    max_length = 500
     if len(combined_text) > max_length:
         combined_text = combined_text[:max_length] + "..."
 
-    # Generate a response using Gemini
     try:
         response = genai.generate_text(
             model="models/text-bison-001",
@@ -87,71 +88,20 @@ def handle_query(query, faiss_index, embeddings_texts, model):
         )
         generated_text = response.result if response else "No response generated."
     except Exception as e:
-        print(f"Error generating text: {e}")
-        generated_text = "An error occurred while generating the response."
-
-    # Update conversation history
-    conversation_history.append(f"User: {query}")
-    conversation_history.append(f"System: {generated_text}")
+        generated_text = f"An error occurred while generating the response: {str(e)}"
 
-    # Extract sources
     sources = [url for _, url in relevant_texts]
-
     return generated_text, sources
 
-def generate_concise_response(prompt, context):
-    try:
-        response = genai.generate_text(
-            model="models/text-bison-001",
-            prompt=f"{prompt}\n\nContext: {context}\n\nAnswer:",
-            max_output_tokens=200
-        )
-        return response.result if response else "No response generated."
-    except Exception as e:
-        print(f"Error generating concise response: {e}")
-        return "An error occurred while generating the concise response."
-
-# Main function to execute the pipeline
 def chatbot(message, history):
-    global conversation_history
-
-    lecture_notes = fetch_lecture_notes()
-    model_architectures = fetch_model_architectures()
-
-    all_texts = lecture_notes + [model_architectures]
-
-    # Load the SentenceTransformers model
-    embedding_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
-
-    embeddings = create_embeddings(all_texts, embedding_model)
-
-    # Initialize FAISS index
-    faiss_index = initialize_faiss_index(np.array(embeddings))
-
     response, sources = handle_query(message, faiss_index, all_texts, embedding_model)
-    print("Query:", message)
-    print("Response:", response)
+    
     total_text = response if response else "No response generated."
     if sources:
-        print("Sources:", sources)
-        relevant_source = "" 
-        for source in sources:
-            relevant_source += source + "\n"
-        total_text += "\n\nSources:\n" + relevant_source
-    else:
-        print("Sources: None of the provided sources were used.")
-    print("----")
-
-    # Generate a concise and relevant summary using Gemini
-    prompt = "Summarize the user queries so far"
-    user_queries_summary = " ".join([message])
-    concise_response = generate_concise_response(prompt, user_queries_summary)
-    print("Concise Response:")
-    print(concise_response)
+        relevant_source = "\n".join(sources)
+        total_text += f"\n\nSources:\n{relevant_source}"
     
-    # Update conversation history with the new user message and system response
     history.append((message, total_text))
-    
     return history
 
 iface = gr.ChatInterface(
@@ -163,11 +113,11 @@ iface = gr.ChatInterface(
         "Explain the transformer architecture.",
         "Tell me about datasets used to train LLMs.",
         "How are LLM training datasets cleaned and preprocessed?",
-        "Summarize the user queries so far"
     ],
     retry_btn="Regenerate",
     undo_btn="Undo",
     clear_btn="Clear",
+    cache_examples=False,  # Disable example caching to avoid file-related errors
 )
 
 if __name__ == "__main__":