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import streamlit as st
import os
import requests
# Define Hugging Face API details
API_URL = "https://api-inference.huggingface.co/models/Huzaifa367/chat-summarizer"
API_TOKEN = os.getenv("AUTH_TOKEN")
HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}
# Function to query Hugging Face API
def query_huggingface(payload):
try:
response = requests.post(API_URL, headers=HEADERS, json=payload)
response.raise_for_status() # Raise exception for non-2xx status codes
return response.json()
except requests.exceptions.RequestException as e:
st.error(f"Error querying Hugging Face API: {e}")
return {"summary_text": f"Error querying Hugging Face API: {e}"}
def main():
st.set_page_config(layout="centered")
st.title("Chat Summarizer")
# User input for chat message
user_message = st.text_input("User Message", "Enter your message here...")
# Process user input and query Hugging Face API
if st.button("Summarize"):
if user_message:
# Construct input text for summarization (no system message)
input_text = f"User: {user_message}"
# Query Hugging Face API for summarization
payload = {"inputs": input_text}
response = query_huggingface(payload)
# Extract summary text from the API response
summary_text = response.get("summary_text", "")
# Display summary text
st.text_area("Summary", value=summary_text)
with st.sidebar:
st.header("Chat with PDF")
# st.title("Menu:")
pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit Button", accept_multiple_files=True, type=["pdf"])
if st.button("Submit"):
with st.spinner("Processing..."):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
get_vector_store(text_chunks, api_key)
st.success("Done")
if st.button("Chat Summarizer"):
st.switch_page('app.py')
# Check if any document is uploaded
if pdf_docs:
user_question = st.text_input("Ask a question from the Docs")
if user_question:
user_input(user_question, api_key)
else:
st.write("Please upload a document first to ask questions.")
if __name__ == "__main__":
main()
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