chatbot.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig
import torch

# ================= CACHE THE MODEL =================
@st.cache_resource
def load_model():
    model_id = "ammoncoder123/IPTchatbotModel1-1.7B"  # ← Your correct model repo

    # 4-bit quantization for memory efficiency (required for 1.7B on GPU)
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float16
    )

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        quantization_config=quantization_config,
        device_map="auto",          # Automatically uses GPU if available
        torch_dtype=torch.float16,
        trust_remote_code=True      # Sometimes needed for custom models
    )

    return pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        max_new_tokens=300,
        temperature=0.7,
        do_sample=True,
        top_p=0.9
    )

# Load model once (this will run on first use)
pipe = load_model()

# ==================== CHAT INTERFACE ====================
st.title("IPT Chatbot (1.7B Fine-Tuned Model)")

# Show a disclaimer
st.info("⚠️ This is a small fine-tuned model (1.7B parameters). Answers may contain inaccuracies. Always verify important information.")

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat history
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# User input
if prompt := st.chat_input("Ask me about IPT, ICT, or anything else..."):
    # Add user message
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    # Generate response
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            # Use proper chat format for Instruct models
            chat_messages = [
                {"role": "user", "content": prompt}
            ]

            outputs = pipe(
                chat_messages,
                max_new_tokens=300,
                temperature=0.7,
                do_sample=True,
                top_p=0.9
            )

            # Extract generated text
            response = outputs[0]["generated_text"]

            # Clean up echoed prompt
            if isinstance(response, str) and response.startswith(prompt):
                response = response[len(prompt):].strip()

            st.markdown(response)

    # Save assistant response
    st.session_state.messages.append({"role": "assistant", "content": response})

# Optional: Clear chat button
if st.button("Clear Conversation"):
    st.session_state.messages = []
    st.rerun()