updated

app.py

@@ -1,21 +1,38 @@
-# app.py at the root
+# app.py
 import gradio as gr
-from src.predict import chatbot_response
+import joblib
+from src.preprocess import clean_text
+
+# Load trained model and responses
+model = joblib.load("models/lms_chatbot.joblib")
+responses = joblib.load("models/responses.joblib")
 
 # Keep conversation history
 history = []
 
-def chat(user_input):
-    global history
-    response = chatbot_response(user_input)
-    history.append(("You", user_input))
-    history.append(("Bot", response))
-    return history, history
+def chatbot_response(user_input):
+    clean_input = clean_text(user_input)
+    tag = model.predict([clean_input])[0]
+    bot_reply = responses.get(tag, ["Sorry, I don't understand."])[0]
+    
+    # Append to history
+    history.append((user_input, bot_reply))
+    
+    # Format chat history nicely
+    formatted_history = ""
+    for user_msg, bot_msg in history:
+        formatted_history += f"**You:** {user_msg}\n**Bot:** {bot_msg}\n\n"
+    
+    return formatted_history
 
-# Gradio Chat Interface
-demo = gr.ChatInterface(fn=chat, 
-                        title="LMS Chatbot", 
-                        description="Chat with the LMS Chatbot", 
-                        height=600)
+# Gradio Interface
+demo = gr.Interface(
+    fn=chatbot_response,
+    inputs=gr.Textbox(lines=2, placeholder="Type your message here..."),
+    outputs=gr.Markdown(),
+    title="LMS Chatbot",
+    description="Ask anything about LMS and get automated responses."
+)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()