Update app.py

app.py

@@ -215,7 +215,7 @@ def google_search(term, num_results=5, lang="en", timeout=5, safe="active", ssl_
             print("  No text extracted")
     return all_results
 
-def process_question(question, documents, history, temperature, top_p, repetition_penalty):
+def process_question(question, documents, history, temperature, top_p, repetition_penalty, enable_web_search):
     global conversation_history
 
     embeddings = get_embeddings()
@@ -223,26 +223,24 @@ def process_question(question, documents, history, temperature, top_p, repetitio
     # Check the memory database for similar questions
     for prev_question, prev_answer in memory_database.items():
         similarity = get_similarity(question, prev_question)
-        if similarity > 0.7:
+        if similarity > 0.8:
             return prev_answer
 
-    # Load the FAISS vector store if it exists
+    # Retrieve relevant documents from the vector store
     if os.path.exists("faiss_database"):
         db = FAISS.load_local("faiss_database", embeddings, allow_dangerous_deserialization=True)
         relevant_docs = db.similarity_search(question, k=3)
     else:
         relevant_docs = []
 
-    if len(relevant_docs) == 0:
-        # Perform web search and update the vector store
+    # Perform web search if enabled and no relevant documents found
+    if enable_web_search and len(relevant_docs) == 0:
         web_search_results = google_search(question, num_results=5)
         web_docs = [Document(page_content=res["text"] or "", metadata={"source": res["link"]}) for res in web_search_results if res["text"]]
 
         if web_docs:
             # Update the FAISS vector store with new documents
             create_or_update_database(web_docs, embeddings)
-
-            # Reload the updated FAISS store and retrieve relevant documents
             db = FAISS.load_local("faiss_database", embeddings, allow_dangerous_deserialization=True)
             relevant_docs = db.similarity_search(question, k=3)
 
@@ -251,7 +249,7 @@ def process_question(question, documents, history, temperature, top_p, repetitio
     if is_related_to_history(question, history):
         context = "None"
     else:
-        history_text = "\n".join([f"Q: {h['question']}\nA: {h['answer']}" for h in history])
+        history_text = "\n".join([f"Q: {h['question']}\nA: {h['answer']}" for h in history]) if history else "None"
         context = context if context else "None"
 
     prompt_text = ChatPromptTemplate(
@@ -306,43 +304,40 @@ def export_memory_db_to_excel():
     return excel_path
 
 with gr.Blocks() as demo:
-    with gr.Tab("Upload PDF"):
-        with gr.Row():
-            pdf_file = gr.File(label="Upload PDF")
-        with gr.Row():
-            recursive_check = gr.Checkbox(label="Use Recursive Text Splitter")
-            upload_button = gr.Button("Upload and Process")
-        with gr.Row():
-            upload_output = gr.Textbox(label="Upload Output")
+    with gr.Row():
+        pdf_file = gr.File(label="Upload PDF")
+    with gr.Row():
+        recursive_check = gr.Checkbox(label="Use Recursive Text Splitter")
+        upload_button = gr.Button("Upload and Process")
+    with gr.Row():
+        upload_output = gr.Textbox(label="Upload Output")
     
-    with gr.Tab("Ask Questions"):
-        with gr.Row():
-            question = gr.Textbox(label="Your Question")
-        with gr.Row():
-            temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, label="Temperature")
-            top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.9, label="Top P")
-            repetition_penalty = gr.Slider(minimum=0.0, maximum=2.0, value=1.0, label="Repetition Penalty")
-        with gr.Row():
-            ask_button = gr.Button("Ask")
-        with gr.Row():
-            answer = gr.Textbox(label="Answer")
+    with gr.Row():
+        question = gr.Textbox(label="Your Question")
+    with gr.Row():
+        temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, label="Temperature")
+        top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.9, label="Top P")
+        repetition_penalty = gr.Slider(minimum=0.0, maximum=2.0, value=1.0, label="Repetition Penalty")
+        web_search_check = gr.Checkbox(label="Enable Web Search")
+    with gr.Row():
+        ask_button = gr.Button("Ask")
+    with gr.Row():
+        answer = gr.Textbox(label="Answer")
     
-    with gr.Tab("Clear Cache"):
-        with gr.Row():
-            clear_button = gr.Button("Clear Cache")
-        with gr.Row():
-            clear_output = gr.Textbox(label="Clear Output")
-
-    with gr.Tab("Export Data"):
-        with gr.Row():
-            export_db_button = gr.Button("Export Database to Excel")
-            export_db_output = gr.Textbox(label="Export Output")
-        with gr.Row():
-            export_memory_button = gr.Button("Export Memory DB to Excel")
-            export_memory_output = gr.Textbox(label="Export Output")
+    with gr.Row():
+        clear_button = gr.Button("Clear Cache")
+    with gr.Row():
+        clear_output = gr.Textbox(label="Clear Output")
+
+    with gr.Row():
+        export_db_button = gr.Button("Export Database to Excel")
+        export_db_output = gr.Textbox(label="Export Output")
+    with gr.Row():
+        export_memory_button = gr.Button("Export Memory DB to Excel")
+        export_memory_output = gr.Textbox(label="Export Output")
     
     upload_button.click(process_uploaded_file, [pdf_file, recursive_check], upload_output)
-    ask_button.click(process_question, [question, pdf_file, recursive_check, temperature, top_p, repetition_penalty], answer)
+    ask_button.click(process_question, [question, pdf_file, conversation_history, temperature, top_p, repetition_penalty, web_search_check], answer)
     clear_button.click(clear_cache, [], clear_output)
     export_db_button.click(extract_db_to_excel, [], export_db_output)
     export_memory_button.click(export_memory_db_to_excel, [], export_memory_output)