Update app.py

app.py

@@ -3,15 +3,7 @@ import gradio as gr
 from transformers import pipeline
 from sentence_transformers import SentenceTransformer, util
 import PyPDF2
-
-# Set up logging with immediate writing
-logging.basicConfig(
-    filename='support_bot_log.txt',
-    level=logging.INFO,
-    format='%(asctime)s - %(message)s',
-    force=True  # Ensures any existing handlers are replaced and logging starts fresh
-)
-logger = logging.getLogger()
+import os
 
 # Load models
 qa_model = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
@@ -39,39 +31,38 @@ def find_relevant_section(query, sections, section_embeddings):
     
     SIMILARITY_THRESHOLD = 0.4
     if similarity_score >= SIMILARITY_THRESHOLD:
-        logger.info(f"Found relevant section using embeddings for query: {query}")
+        log_message(f"Found relevant section using embeddings for query: {query}")
         return best_section
     
-    logger.info(f"Low similarity ({similarity_score}). Falling back to keyword search.")
+    log_message(f"Low similarity ({similarity_score}). Falling back to keyword search.")
     
     # Keyword-based fallback search with stopword filtering
-    query_words = {word for word in query.lower().split() if word not in stopwords}
-    for section in sections:
+    query_words = {word for word in sections:
         section_words = {word for word in section.lower().split() if word not in stopwords}
         common_words = query_words.intersection(section_words)
         if len(common_words) >= 2:
-            logger.info(f"Keyword match found for query: {query} with common words: {common_words}")
+            log_message(f"Keyword match found for query: {query} with common words: {common_words}")
             return section
     
-    logger.info(f"No good keyword match found. Returning default fallback response.")
+    log_message(f"No good keyword match found. Returning default fallback response.")
     return "I don’t have enough information to answer that."
 
 # Process the uploaded file with detailed logging
 def process_file(file, state):
     if file is None:
-        logger.info("No file uploaded.")
+        log_message("No file uploaded.")
         return [("Bot", "Please upload a file.")], state
     
     file_path = file.name
     if file_path.lower().endswith(".pdf"):
-        logger.info(f"Uploaded PDF file: {file_path}")
+        log_message(f"Uploaded PDF file: {file_path}")
         text = extract_text_from_pdf(file_path)
     elif file_path.lower().endswith(".txt"):
-        logger.info(f"Uploaded TXT file: {file_path}")
+        log_message(f"Uploaded TXT file: {file_path}")
         with open(file_path, 'r', encoding='utf-8') as f:
             text = f.read()
     else:
-        logger.error(f"Unsupported file format: {file_path}")
+        log_message(f"Unsupported file format: {file_path}")
         return [("Bot", "Unsupported file format. Please upload a PDF or TXT file.")], state
     
     sections = text.split('\n\n')
@@ -83,18 +74,18 @@ def process_file(file, state):
     state['feedback_count'] = 0
     state['mode'] = 'waiting_for_query'
     state['chat_history'] = [("Bot", "File processed. You can now ask questions.")]
-    logger.info(f"Processed file: {file_path}")
+    log_message(f"Processed file: {file_path}")
     return state['chat_history'], state
 
 # Handle user input (queries and feedback)
 def handle_input(user_input, state):
     if state['mode'] == 'waiting_for_upload':
         state['chat_history'].append(("Bot", "Please upload a file first."))
-        logger.info("User attempted to interact without uploading a file.")
+        log_message("User attempted to interact without uploading a file.")
         return state['chat_history'], state
     elif state['mode'] == 'waiting_for_query':
         if user_input.lower() == "exit":
-            logger.info("User entered 'exit'. Ending session.")
+            log_message("User entered 'exit'. Ending session.")
             state['mode'] = 'exited'
             state['chat_history'].append(("User", "exit"))
             state['chat_history'].append(("Bot", "Session ended. You can download the log file."))
@@ -114,10 +105,10 @@ def handle_input(user_input, state):
         state['chat_history'].append(("User", query))
         state['chat_history'].append(("Bot", f"Answer: {answer}\nPlease provide feedback: good, too vague, not helpful."))
         # Log the query and initial answer here:
-        logger.info(f"Query: {query}, Answer: {answer}")
+        log_message(f"Query: {query}, Answer: {answer}")
     elif state['mode'] == 'waiting_for_feedback':
         if user_input.lower() == "exit":
-            logger.info("User entered 'exit'. Ending session.")
+            log_message("User entered 'exit'. Ending session.")
             state['mode'] = 'exited'
             state['chat_history'].append(("User", "exit"))
             state['chat_history'].append(("Bot", "Session ended. You can download the log file."))
@@ -125,15 +116,15 @@ def handle_input(user_input, state):
         
         feedback = user_input.lower()
         state['chat_history'].append(("User", feedback))
-        logger.info(f"Feedback: {feedback}")
+        log_message(f"Feedback: {feedback}")
         if feedback == "good" or state['feedback_count'] >= 2:
             state['mode'] = 'waiting_for_query'
             if feedback == "good":
                 state['chat_history'].append(("Bot", "Thank you for your feedback. You can ask another question."))
-                logger.info("Feedback accepted as 'good'. Waiting for next query.")
+                log_message("Feedback accepted as 'good'. Waiting for next query.")
             else:
                 state['chat_history'].append(("Bot", "Maximum feedback iterations reached. You can ask another question."))
-                logger.info("Max feedback iterations reached. Waiting for next query.")
+                log_message("Max feedback iterations reached. Waiting for next query.")
         else:
             query = state['current_query']
             context = find_relevant_section(query, state['sections'], state['section_embeddings'])
@@ -143,15 +134,15 @@ def handle_input(user_input, state):
                 adjusted_answer = qa_model(question=query + " Please provide more detailed information with examples.", context=context)['answer']
             else:
                 state['chat_history'].append(("Bot", "Please provide valid feedback: good, too vague, not helpful."))
-                logger.info(f"Invalid feedback received: {feedback}")
+                log_message(f"Invalid feedback received: {feedback}")
                 return state['chat_history'], state
             state['last_answer'] = adjusted_answer
             state['feedback_count'] += 1
             state['chat_history'].append(("Bot", f"Updated answer: {adjusted_answer}\nPlease provide feedback: good, too vague, not helpful."))
-            logger.info(f"Adjusted answer: {adjusted_answer}")
+            log_message(f"Adjusted answer: {adjusted_answer}")
     elif state['mode'] == 'exited':
         state['chat_history'].append(("Bot", "Session is over. Please download the log."))
-        logger.info("User interacted after exiting.")
+        log_message("User interacted after exiting.")
     return state['chat_history'], state
 
 # Initial state
@@ -163,9 +154,22 @@ initial_state = {
     'feedback_count': 0,
     'mode': 'waiting_for_upload',
     'chat_history': [("Bot", "Please upload a PDF or TXT file to start.")],
-    'last_answer': None
+    'last_answer': None,
+    'log_messages':# Initialize an empty list for log messages
 }
 
+# Logging function to store messages in memory
+def log_message(message):
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    log_entry = f"{timestamp} - {message}"
+    initial_state['log_messages'].append(log_entry)
+
+# Function to save logs to file
+def save_logs_to_file():
+    with open("support_bot_log.txt", "w") as log_file:
+        for log_message in initial_state['log_messages']:
+            log_file.write(log_message + "\n")
+
 # Gradio interface
 with gr.Blocks() as demo:
     state = gr.State(initial_state)
@@ -173,7 +177,7 @@ with gr.Blocks() as demo:
     chat = gr.Chatbot()
     user_input = gr.Textbox(label="Your query or feedback")
     submit_btn = gr.Button("Submit")
-    log_file = gr.File(label="Download Log File", value="support_bot_log.txt")  # Added for log download
+    log_file = gr.File(label="Download Log File")  # Changed: No initial value
 
     # Process file upload
     file_upload.upload(process_file, inputs=[file_upload, state], outputs=[chat, state])
@@ -181,4 +185,17 @@ with gr.Blocks() as demo:
     # Handle user input and clear the textbox
     submit_btn.click(handle_input, inputs=[user_input, state], outputs=[chat, state]).then(lambda: "", None, user_input)
 
+    # Update the log file just before download
+    log_file.click(save_logs_to_file,, [log_file])  # Trigger save on click
+    
+    # Also save logs when user exits
+    user_input.submit(
+        lambda user_input, state: (
+            save_logs_to_file() if user_input.lower() == "exit" else None,
+            state
+        ),
+        [user_input, state],
+        [log_file, state]
+    )
+
 demo.launch(share=True)