Update app.py

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.llms import HuggingFacePipeline
+import torch
+from transformers import pipeline
+
+# Load a smaller LLM (e.g., Zephyr-7B or Mistral-7B)
+def load_llm():
+    model_name = "HuggingFaceH4/zephyr-7b-alpha"  # Replace with your preferred model
+    pipe = pipeline("text-generation", model=model_name, torch_dtype=torch.float16, device_map="auto")
+    llm = HuggingFacePipeline(pipeline=pipe)
+    return llm
+
+# Extract text from PDF
+def extract_text_from_pdf(file):
+    reader = PdfReader(file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+# Split text into chunks
+def split_text(text, chunk_size=1000, chunk_overlap=200):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+    chunks = splitter.split_text(text)
+    return chunks
+
+# Create embeddings and vector store
+def create_vector_store(chunks):
+    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    vector_store = FAISS.from_texts(chunks, embeddings)
+    return vector_store
+
+# Query the PDF
+def query_pdf(vector_store, query, llm):
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vector_store.as_retriever())
+    result = qa.run(query)
+    return result
+
+# Streamlit App
+def main():
+    st.title("Chat with PDF")
+    st.write("Upload a PDF and ask questions about it!")
+
+    # File uploader
+    uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
+    if uploaded_file is None:
+        st.info("Using default PDF.")
+        uploaded_file = "default.pdf"  # Add a default PDF
+
+    # Extract text
+    text = extract_text_from_pdf(uploaded_file)
+
+    # Split text into chunks
+    chunks = split_text(text)
+
+    # Create vector store
+    vector_store = create_vector_store(chunks)
+
+    # Load LLM
+    llm = load_llm()
+
+    # Query translation options
+    query_method = st.selectbox(
+        "Query Translation Method",
+        ["Multi-Query", "RAG Fusion", "Decomposition", "Step Back", "HyDE"],
+        help="Choose a method to improve query retrieval."
+    )
+
+    # User input
+    query = st.text_input("Ask a question about the PDF:")
+    if query:
+        # Query the PDF
+        result = query_pdf(vector_store, query, llm)
+        st.write("**Answer:**", result["answer"])
+        st.write("**Source Text:**", result["source_text"])
+
+if __name__ == "__main__":
+    main()