Update app.py

app.py

@@ -4,13 +4,14 @@ from transformers import pipeline
 from sentence_transformers import SentenceTransformer, util
 import PyPDF2
 
-# Set up logging with immediate flushing
+# 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()
-logger.setLevel(logging.INFO)
-handler = logging.FileHandler('support_bot_log.txt', mode='a', buffering=1)  # Line-buffered for immediate writes
-formatter = logging.Formatter('%(asctime)s - %(message)s')
-handler.setFormatter(formatter)
-logger.addHandler(handler)
 
 # Load models
 qa_model = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
@@ -38,10 +39,10 @@ def find_relevant_section(query, sections, section_embeddings):
     
     SIMILARITY_THRESHOLD = 0.4
     if similarity_score >= SIMILARITY_THRESHOLD:
-        logging.info(f"Found relevant section using embeddings for query: {query}")
+        logger.info(f"Found relevant section using embeddings for query: {query}")
         return best_section
     
-    logging.info(f"Low similarity ({similarity_score}). Falling back to keyword search.")
+    logger.info(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}
@@ -49,28 +50,28 @@ def find_relevant_section(query, sections, section_embeddings):
         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:
-            logging.info(f"Keyword match found for query: {query} with common words: {common_words}")
+            logger.info(f"Keyword match found for query: {query} with common words: {common_words}")
             return section
     
-    logging.info(f"No good keyword match found. Returning default fallback response.")
+    logger.info(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:
-        logging.info("No file uploaded.")
+        logger.info("No file uploaded.")
         return [("Bot", "Please upload a file.")], state
     
     file_path = file.name
     if file_path.lower().endswith(".pdf"):
-        logging.info(f"Uploaded PDF file: {file_path}")
+        logger.info(f"Uploaded PDF file: {file_path}")
         text = extract_text_from_pdf(file_path)
     elif file_path.lower().endswith(".txt"):
-        logging.info(f"Uploaded TXT file: {file_path}")
+        logger.info(f"Uploaded TXT file: {file_path}")
         with open(file_path, 'r', encoding='utf-8') as f:
             text = f.read()
     else:
-        logging.error(f"Unsupported file format: {file_path}")
+        logger.error(f"Unsupported file format: {file_path}")
         return [("Bot", "Unsupported file format. Please upload a PDF or TXT file.")], state
     
     sections = text.split('\n\n')
@@ -82,14 +83,14 @@ 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.")]
-    logging.info(f"Processed file: {file_path}")
+    logger.info(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."))
-        logging.info("User attempted to interact without uploading a file.")
+        logger.info("User attempted to interact without uploading a file.")
     elif state['mode'] == 'waiting_for_query':
         query = user_input
         state['current_query'] = query
@@ -104,17 +105,19 @@ def handle_input(user_input, state):
         state['mode'] = 'waiting_for_feedback'
         state['chat_history'].append(("User", query))
         state['chat_history'].append(("Bot", f"Answer: {answer}\nPlease provide feedback: good, too vague, not helpful."))
-        logging.info(f"Query: {query}, Answer: {answer}")
+        logger.info(f"Query: {query}, Answer: {answer}")
     elif state['mode'] == 'waiting_for_feedback':
         feedback = user_input.lower()
         state['chat_history'].append(("User", feedback))
-        logging.info(f"Feedback: {feedback}")
+        logger.info(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.")
             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.")
         else:
             query = state['current_query']
             context = find_relevant_section(query, state['sections'], state['section_embeddings'])
@@ -124,11 +127,12 @@ 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}")
                 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."))
-            logging.info(f"Adjusted answer: {adjusted_answer}")
+            logger.info(f"Adjusted answer: {adjusted_answer}")
     return state['chat_history'], state
 
 # Initial state