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import streamlit as st
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import os
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import faiss
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import logging
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.vectorstores import FAISS
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from langchain_community.llms import HuggingFacePipeline
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from langchain.chains import RetrievalQA
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from ingest import create_faiss_index
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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checkpoint = "LaMini-T5-738M"
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@st.cache_resource
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def load_llm():
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
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pipe = pipeline(
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'text2text-generation',
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model=model,
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tokenizer=tokenizer,
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max_length=256,
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do_sample=True,
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temperature=0.3,
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top_p=0.95
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)
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return HuggingFacePipeline(pipeline=pipe)
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def validate_index_file(index_path):
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try:
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with open(index_path, 'rb') as f:
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data = f.read(100)
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logger.info(f"Successfully read {len(data)} bytes from the index file")
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return True
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except Exception as e:
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logger.error(f"Error validating index file: {e}")
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return False
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def load_faiss_index():
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index_path = "faiss_index/index.faiss"
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if not os.path.exists(index_path):
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st.warning("Index file not found. Creating a new one...")
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create_faiss_index()
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if not os.path.exists(index_path):
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st.error("Failed to create the FAISS index. Please check the 'docs' directory and try again.")
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raise RuntimeError("FAISS index creation failed.")
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try:
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index = faiss.read_index(index_path)
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if index is None:
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raise ValueError("Failed to read FAISS index.")
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embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
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db = FAISS.load_local("faiss_index", embeddings)
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if db.index is None or db.index_to_docstore_id is None:
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raise ValueError("FAISS index or docstore_id mapping is None.")
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return db.as_retriever()
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except Exception as e:
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st.error(f"Failed to load FAISS index: {e}")
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logger.exception("Exception in load_faiss_index")
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raise
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def process_answer(instruction):
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try:
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retriever = load_faiss_index()
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llm = load_llm()
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qa = RetrievalQA.from_chain_type(
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llm=llm,
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chain_type="stuff",
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retriever=retriever,
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return_source_documents=True
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)
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generated_text = qa.invoke(instruction)
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answer = generated_text['result']
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return answer, generated_text
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except Exception as e:
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st.error(f"An error occurred while processing the answer: {e}")
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logger.exception("Exception in process_answer")
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return "An error occurred while processing your request.", {}
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def main():
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st.title("Search Your PDF 📚📝")
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with st.expander("About the App"):
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st.markdown(
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"""
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This is a Generative AI powered Question and Answering app that responds to questions about your PDF File.
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"""
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)
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question = st.text_area("Enter your Question")
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if st.button("Ask"):
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st.info("Your Question: " + question)
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st.info("Your Answer")
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try:
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answer, metadata = process_answer(question)
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st.write(answer)
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st.write(metadata)
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except Exception as e:
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st.error(f"An unexpected error occurred: {e}")
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logger.exception("Unexpected error in main function")
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if __name__ == '__main__':
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main() |
