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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.")
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