Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,147 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import asyncio
+import tempfile
+import os
+import fitz  # PyMuPDF
+import io
 import streamlit as st
+from PIL import Image
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain_community.llms import HuggingFacePipeline
+from transformers import AutoTokenizer, pipeline, AutoModelForSeq2SeqLM
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Fix for event loop issues
+if os.name == 'nt':
+    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+
+MODEL_NAME = "google/flan-t5-base"
+EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+CHUNK_SIZE = 500
+CHUNK_OVERLAP = 50
+
+def initialize_general_model():
+    """Initialize the model for general knowledge questions"""
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+    
+    return pipeline(
+        "text2text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        max_length=256,
+        temperature=0,
+        repetition_penalty=1.2
+    )
+
+def create_vector_store(pdf_path):
+    """Process PDF and create FAISS vector store"""
+    loader = PyPDFLoader(pdf_path)
+    pages = loader.load_and_split()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=CHUNK_SIZE,
+        chunk_overlap=CHUNK_OVERLAP
+    )
+    texts = text_splitter.split_documents(pages)
+
+    embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
+    return FAISS.from_documents(texts, embeddings)
+
+def create_qa_chain(vectorstore):
+    """Create the Retrieval QA chain for PDF content"""
+    pipe = initialize_general_model()
+    llm = HuggingFacePipeline(pipeline=pipe)
+    
+    return RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vectorstore.as_retriever(),
+        return_source_documents=True
+    )
+
+def render_pdf_page(pdf_bytes, page_number):
+    """Render specific PDF page as image"""
+    doc = fitz.open(stream=pdf_bytes, filetype="pdf")
+    page = doc.load_page(page_number)
+    pix = page.get_pixmap()
+    img_bytes = pix.tobytes()
+    return Image.open(io.BytesIO(img_bytes))
+
+def main():
+    st.title("VectorAsk")
+    st.write("Get answers with source page images!")
+    
+    # Initialize session states
+    if 'pdf_bytes' not in st.session_state:
+        st.session_state.pdf_bytes = None
+
+    mode = st.radio("Select answer source:",
+                    ("PDF Content", "Text input"),
+                    horizontal=True)
+
+    if mode == "PDF Content":
+        uploaded_file = st.file_uploader("Upload PDF", type="pdf")
+        if uploaded_file is not None:
+            st.session_state.pdf_bytes = uploaded_file.getvalue()
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
+                tmp_file.write(st.session_state.pdf_bytes)
+                tmp_path = tmp_file.name
+
+            with st.spinner("Processing PDF..."):
+                vectorstore = create_vector_store(tmp_path)
+                os.remove(tmp_path)
+                st.session_state['qa_chain'] = create_qa_chain(vectorstore)
+
+    question = st.text_input("Enter your question:")
+    
+    if question:
+        with st.spinner("Generating answer..."):
+            if mode == "General Knowledge":
+                if 'general_pipe' not in st.session_state:
+                    st.session_state.general_pipe = initialize_general_model()
+                
+                result = st.session_state.general_pipe(
+                    question,
+                    max_length=256,
+                    temperature=0
+                )[0]['generated_text']
+                
+                st.subheader("Answer:")
+                st.write(result)
+                st.info("This answer is generated from the model's general knowledge")
+                
+            elif mode == "PDF Content":
+                if 'qa_chain' not in st.session_state:
+                    st.warning("Please upload a PDF file first!")
+                    return
+                
+                result = st.session_state['qa_chain']({"query": question})
+                
+                # Display answer
+                st.subheader("Answer:")
+                st.write(result["result"])
+                
+                # Display source documents with images
+                st.subheader("Source Evidence:")
+                for doc in result["source_documents"]:
+                    page_num = doc.metadata['page']
+                    
+                    col1, col2 = st.columns([2, 3])
+                    with col1:
+                        try:
+                            img = render_pdf_page(st.session_state.pdf_bytes, page_num)
+                            st.image(img, caption=f"Page {page_num + 1}", use_column_width=True)
+                        except Exception as e:
+                            st.error(f"Error rendering page: {str(e)}")
+                    
+                    with col2:
+                        st.write(f"**Page {page_num + 1} Content:**")
+                        st.write(doc.page_content)
+                    
+                    st.write("---")
+
+if __name__ == "__main__":
+    main()