Update app.py

app.py

@@ -5,7 +5,7 @@ from transformers import pipeline
 from sentence_transformers import SentenceTransformer, util
 import PyPDF2
 
-# Set up logging: we write logs to /tmp so that it's writable on Spaces.
+# Set up logging: write logs to a writable directory (/tmp)
 log_file_path = "/tmp/support_bot_log.txt"
 logging.basicConfig(filename=log_file_path, level=logging.INFO, format='%(asctime)s - %(message)s')
 
@@ -45,7 +45,7 @@ class SupportBotAgent:
 
     def find_relevant_section(self, query):
         """
-        First uses semantic similarity. If similarity is too low, falls back to a keyword search.
+        Uses semantic similarity first, falling back to keyword search if needed.
         """
         stopwords = {"and", "the", "is", "for", "to", "a", "an", "of", "in", "on", "at", "with", "by", "it", "as", "so", "what"}
         query_embedding = self.embedder.encode(query, convert_to_tensor=True)
@@ -65,10 +65,10 @@ class SupportBotAgent:
             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}")
+                logging.info(f"Keyword match for query: {query} with common words: {common_words}")
                 return section
 
-        logging.info(f"No good keyword match found. Returning default fallback response.")
+        logging.info("No good keyword match found. Returning default response.")
         return "I don’t have enough information to answer that."
 
     def answer_query(self, query):
@@ -82,7 +82,7 @@ class SupportBotAgent:
         return answer
 
     def adjust_response(self, query, response, feedback):
-        """Modify the response based on user feedback."""
+        """Adjusts the response based on feedback."""
         if feedback == "too vague":
             context = self.find_relevant_section(query)
             adjusted_response = f"{response}\n\n(More details:\n{context[:500]}...)"
@@ -96,18 +96,28 @@ class SupportBotAgent:
 # --- Gradio Functions and App Workflow ---
 
 def process_file(file, state):
-    """Handles the file upload and initializes the SupportBotAgent."""
+    """Handles file upload and initializes the SupportBotAgent."""
+    logging.info("Received file upload request")
     if file is None:
         logging.info("No file uploaded")
         return [("Bot", "Please upload a TXT or PDF file.")], state
-    # Save the uploaded file to /tmp
+
+    # Save the uploaded file to /tmp. Handle both file objects and NamedString.
     temp_path = os.path.join("/tmp", file.name)
     with open(temp_path, "wb") as f:
-        f.write(file.read())
+        if hasattr(file, "read"):
+            content = file.read()
+        else:
+            content = file
+        if isinstance(content, str):
+            content = content.encode("utf-8")
+        f.write(content)
+
     try:
         state["agent"] = SupportBotAgent(temp_path)
     except Exception as e:
         return [("Bot", f"Error processing file: {str(e)}")], state
+
     state["chat_history"] = [("Bot", "File loaded successfully. Enter your query (or type 'exit' to end):")]
     state["mode"] = "query"
     state["last_query"] = ""
@@ -117,15 +127,17 @@ def process_file(file, state):
 
 def process_input(user_input, state):
     """
-    Processes user input as either a query or feedback based on the current mode.
+    Processes user input: as a query or feedback.
     Typing 'exit' stops the session.
     """
     if state.get("mode", "query") == "ended":
         return state["chat_history"], state
+
     if user_input.lower() == "exit":
         state["chat_history"].append(("Bot", "Session ended. You may now download the log file."))
         state["mode"] = "ended"
         return state["chat_history"], state
+
     if state["mode"] == "query":
         state["last_query"] = user_input
         answer = state["agent"].answer_query(user_input)
@@ -156,9 +168,10 @@ with gr.Blocks() as demo:
     chat = gr.Chatbot()
     user_input = gr.Textbox(label="Enter your query or feedback")
     submit_btn = gr.Button("Submit")
+    # Provide a file component to download the log file
     log_file = gr.File(label="Download Log File", file_count="single", interactive=False, value=log_file_path)
 
     file_upload.upload(process_file, inputs=[file_upload, state], outputs=[chat, state])
     submit_btn.click(process_input, inputs=[user_input, state], outputs=[chat, state]).then(lambda: "", None, user_input)
 
-demo.launch(share=True)
+demo.launch(share=True)