Update chatbot.py

chatbot.py

@@ -4,13 +4,13 @@ import torch
 from huggingface_hub import login
 import os
 
-# Force authentication
+# Force authentication with your HF token (secret in Space settings)
 login(token=os.getenv("HF_TOKEN"))
 
 # ================= CACHE THE MODEL =================
 @st.cache_resource
 def load_model():
-    model_id = "ammoncoder123/IPTchatbotModel-1.7B"
+    model_id = "ammoncoder123/IPTchatbotModel1-1.7B"  # Your correct repo
 
     quantization_config = BitsAndBytesConfig(
         load_in_4bit=True,
@@ -39,10 +39,10 @@ def load_model():
 pipe = load_model()
 
 # ==================== CHAT INTERFACE ====================
-st.title("IPTCHATBOT ASSISTANCE")
-
-st.info(" Answers may vary verify facts.")
+st.title("IPT Chatbot Assistance")
+st.info("Answers may vary — please verify important facts.")
 
+# Display chat history
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
@@ -50,30 +50,40 @@ for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
 
-# ... (model loading code above remains unchanged)
-
+# User input
 if prompt := st.chat_input("Ask about Industrial Practical Training..."):
+    # Add user message to history and display
     st.session_state.messages.append({"role": "user", "content": prompt})
     with st.chat_message("user"):
         st.markdown(prompt)
 
+    # Generate assistant response
     with st.chat_message("assistant"):
         with st.spinner("Thinking..."):
-            # Define the system prompt here
-            system_prompt = system_prompt = """
-            You are a helpful assistant ONLY for questions about Industrial Practical Training (IPT) in Tanzania.
-            IPT means Industrial Practical Training (Industrial Attachment) a mandatory work placement for students to gain real-world experience.
-            Always use this correct meaning.
-            If the question is not about IPT, logbooks, reports, placement, or related topics, reply exactly:
-            "Sorry, I can only answer questions about Industrial Practical Training (IPT). Please ask something related."
+            # Strong system prompt (customized for your IPT dataset)
+            system_prompt = """
+            You are a helpful assistant for engineering and ICT students in Tanzania who are preparing for or doing Industrial Practical Training (IPT), also known as Industrial Attachment.
+            IPT means Industrial Practical Training — a mandatory work placement where students gain real-world experience in companies related to their field of study.
+            Always answer questions about:
+            - What IPT is
+            - How to do IPT (logbook, daily/weekly reports, technical report, presentation)
+            - Placement suggestions for different engineering fields (ICT, Mechatronics, Electrical, Mechanical, Civil, Biomedical, etc.)
+            - Choosing IPT centers/companies
+            - Tips for success in IPT
+            - Any other directly related IPT topic
+            If the question is clearly unrelated to IPT (e.g., politics, sports, personal life), politely reply:
+            "Sorry, I can only help with questions about Industrial Practical Training (IPT). Please ask something related to IPT, logbook, placement, or reports."
+            For placement suggestions (e.g., for Mechatronics, Electrical, ICT), give practical, realistic company types or industries in Tanzania that match the field.
+            Be concise, accurate, and helpful.
             """
 
-            # Build messages with system prompt first
+            # Build messages: system prompt first, then user prompt
             chat_messages = [
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": prompt}
             ]
 
+            # Generate response
             outputs = pipe(
                 chat_messages,
                 max_new_tokens=300,
@@ -82,10 +92,18 @@ if prompt := st.chat_input("Ask about Industrial Practical Training..."):
                 top_p=0.9
             )
 
+            # Extract and clean the generated text
             response = outputs[0]["generated_text"]
             if isinstance(response, str) and response.startswith(prompt):
                 response = response[len(prompt):].strip()
 
+            # Show the response
             st.markdown(response)
 
-    st.session_state.messages.append({"role": "assistant", "content": response})
+    # Save assistant response to history
+    st.session_state.messages.append({"role": "assistant", "content": response})
+
+# Optional: Clear conversation button
+if st.button("Clear Conversation"):
+    st.session_state.messages = []
+    st.rerun()