Update app.py

app.py

@@ -43,16 +43,23 @@ def generate_text() -> str:
             user, chatbot, time = row_parts
             text += f"Time: {time}\nUser: {user}\nChatbot: {chatbot}\n\n"
         return text if text else "No messages yet"
-        
 
 def store_message(chatinput: str, chatresponse: str):
     if chatinput and chatresponse:
         with open(DATA_FILE, "a") as file:
             file.write(f"{datetime.now()},{chatinput},{chatresponse}\n")
             print(f"Wrote to datafile: {datetime.now()},{chatinput},{chatresponse}\n")
-        
-        return generate_text()
 
+        # Push back to hub every 10th time the function is called
+    if store_message.count_calls % 10 == 0:
+        print("Pushing back to Hugging Face model hub")
+        # Call the push_to_hub() function to push the changes to the hub
+        push_to_hub(commit_message="Added new chat data")
+    store_message.count_calls += 1
+    
+    return generate_text()
+
+store_message.count_calls = 1  #initiases the count at one. We want to count how many messages stored before pushing back to repo.
 
 #gets the index file which is the context data
 def get_index(index_file_path):
@@ -66,7 +73,7 @@ def get_index(index_file_path):
 
 index = get_index(INDEX_FILE)
 
-# passes the prompt to the chatbot, queries the index, stores the output, returns the response
+# passes the prompt to the chatbot
 def chatbot(input_text, mentioned_person='Mediator John Haynes', confidence_threshold=0.5):
     prompt = f"You are {mentioned_person}. Answer this: {input_text}. Only reply from the contextual data, or say you don't know. At the end of your answer ask an insightful question."
     response = index.query(prompt, response_mode="default")
@@ -87,7 +94,4 @@ iface = Interface(
     title="AI Chatbot trained on J. Haynes mediation material, v0.5",
     description=about)
                                          
-iface.launch()
-
-
-
+iface.launch()