app.py

import streamlit as st
from transformers import pipeline
from langchain_community.llms import HuggingFacePipeline

# Title
st.title("🤖 Hugging Face Chatbot with LangChain")

# Session state for conversation history
if "sessionMessages" not in st.session_state:
    st.session_state.sessionMessages = []

# Build HF pipeline in float32
pipe = pipeline(
    "text-generation",
    model="tiiuae/falcon-7b-instruct",  # or any Hugging Face model
    torch_dtype="float32",              # force safe float32 precision
    device_map="auto"                   # auto-detect GPU/CPU
)

# Wrap into LangChain
chat = HuggingFacePipeline(pipeline=pipe)

# Function to handle answers
def load_answer(question):
    st.session_state.sessionMessages.append({"role": "user", "content": question})
    assistant_answer = chat.invoke(st.session_state.sessionMessages)
    st.session_state.sessionMessages.append({"role": "assistant", "content": assistant_answer})
    return assistant_answer

# Streamlit input
user_input = st.text_input("Ask me anything:")

if user_input:
    response = load_answer(user_input)
    st.write(f"**Assistant:** {response}")

# Display chat history
if st.session_state.sessionMessages:
    st.subheader("Chat History")
    for msg in st.session_state.sessionMessages:
        st.write(f"**{msg['role'].capitalize()}:** {msg['content']}")