app.py

import os
import torch
from huggingface_hub import InferenceClient
import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Load HF_TOKEN securely 
hf_token = os.getenv("HF_TOKEN") 

# Set up the Hugging Face Inference Client with the Bearer token
client = InferenceClient(api_key=f"Bearer {hf_token}")

# Model paths and IDs
model_id = "mistralai/Mistral-7B-Instruct-v0.3"
bart_model_path = "ChijoTheDatascientist/summarization-model"

# Load BART model for summarization
device = torch.device('cpu')
bart_tokenizer = AutoTokenizer.from_pretrained(bart_model_path)
bart_model = AutoModelForSeq2SeqLM.from_pretrained(bart_model_path).to(device)

@st.cache_data
def summarize_review(review_text):
    inputs = bart_tokenizer(review_text, max_length=1024, truncation=True, return_tensors="pt")
    summary_ids = bart_model.generate(inputs["input_ids"], max_length=40, min_length=10, length_penalty=2.0, num_beams=8, early_stopping=True)
    summary = bart_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

def generate_response(system_message, user_input, chat_history, max_new_tokens=128):
    try:
        # Prepare the messages for the Hugging Face Inference API
        messages = [{"role": "user", "content": user_input}]
        
        # Call the Inference API
        completion = client.chat.completions.create(
            model=model_id, 
            messages=messages, 
            max_tokens=max_new_tokens,
        )

        # Get the response from the API
        response = completion.choices[0].message["content"]
        return response

    except Exception as e:
        return f"Error generating response: {e}"

# Streamlit app configuration
st.set_page_config(page_title="Insight Snap & Summarizer")
st.title("Insight Snap & Summarizer")

st.markdown("""
- Use specific keywords in your queries to get targeted responses:
    - **"summarize"**: To summarize customer reviews.
    - **"Feedback or insights"**: Get actionable business insights based on feedback.
""")

# Initialize session state for chat history
if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

# Chat interface
user_input = st.text_area("Enter customer reviews or a question:")

if st.button("Submit"):
    if user_input:
        # Summarize if the query is feedback-related
        if "summarize" in user_input.lower():
            summary = summarize_review(user_input)
            st.markdown(f"**Summary:** \n{summary}")
        elif "insight" in user_input.lower() or "feedback" in user_input.lower():
            system_message = (
                "You are a helpful assistant providing actionable insights "
                "from customer feedback to help businesses improve their services."
            )
            # Use the last summarized text if available
            last_summary = st.session_state.get("last_summary", "")
            query_input = last_summary if last_summary else user_input
            response = generate_response(system_message, query_input, st.session_state.chat_history)
            
            if response:
                # Update chat history
                st.session_state.chat_history.append({"role": "user", "content": user_input})
                st.session_state.chat_history.append({"role": "assistant", "content": response})
                st.markdown(f"**Insight:** \n{response}")
            else:
                st.warning("No response generated. Please try again later.")
        else:
            st.warning("Please specify if you want to 'summarize' or get 'insights'.")

        # Store the last summary for insights
        if "summarize" in user_input.lower():
            st.session_state["last_summary"] = summary
    else:
        st.warning("Please enter customer reviews or ask for insights.")