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# from dotenv import load_dotenv
import os
import streamlit as st
import openai
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document
from sentence_transformers import CrossEncoder
import fitz # PyMuPDF library for PDF processing
import tempfile
# load_dotenv()
openai.api_key = st.secrets['api_key']
# Create a sidebar
st.sidebar.title("Model Configuration")
# File uploader moved to the sidebar
uploaded_file = st.sidebar.file_uploader("Upload a PDF", type=["pdf"])
# Option menu for model selection
model_selection = st.sidebar.selectbox("Model Selection", ["GPT 3.5", "LLama 2"])
# Slider for selecting model temperature
model_temperature = st.sidebar.slider("Select model temperature", 0.0, 0.5, 1.0)
# Initialize LLM response storage
llm_responses = []
# Initialize HHEM model
hhem_model = CrossEncoder('vectara/hallucination_evaluation_model')
if uploaded_file is not None:
# Save the uploaded PDF file to a temporary location
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
temp_pdf.write(uploaded_file.read())
temp_pdf_path = temp_pdf.name
# Open the PDF file using PyMuPDF
pdf_document = fitz.open(temp_pdf_path)
text = ""
for page_number in range(pdf_document.page_count):
page = pdf_document[page_number]
text += page.get_text()
documents = [Document(text=text)]
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
query = st.text_input("Ask your question")
button = st.button("Ask")
if button:
print(query)
response = query_engine.query(query)
st.write(response.response)
# Record LLM response
llm_responses.append(response.response)
# Calculate and display HHEM score for each LLM response
for i, llm_response in enumerate(llm_responses):
score = hhem_model.predict([text, llm_response])
st.sidebar.write(f"Response {i + 1} - HHEM Score: {score}")
# Close and remove the temporary PDF file
pdf_document.close()
os.remove(temp_pdf_path)
# Display LLM responses
if llm_responses:
st.sidebar.markdown("## LLM Responses")
for i, llm_response in enumerate(llm_responses):
st.sidebar.write(f"Response {i + 1}: {llm_response}")
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