Create app.py

app.py

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+import streamlit as st
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.llms import HuggingFacePipeline
+from langchain.schema import Document
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+from sentence_transformers import SentenceTransformer
+import torch
+from io import BytesIO
+import fitz  # PyMuPDF
+
+# Set device based on GPU availability
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load embeddings with a smaller model and run on CPU
+embedding_model = "all-MiniLM-L6-v2"
+embeddings = HuggingFaceEmbeddings(model_name=embedding_model, model_kwargs={'device': 'cpu'})
+
+# Set up text generation model with PyTorch-compatible pipeline
+model_name = "google/flan-t5-small"  # Or use a smaller model if needed
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
+
+# Create a text generation pipeline
+generator = pipeline(
+    "text2text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=0 if device == "cuda" else -1,
+    model_kwargs={"max_length": 256, "temperature": 0.7}
+)
+
+llm = HuggingFacePipeline(pipeline=generator)
+
+# Streamlit interface
+def main():
+    st.title("Chat with Multiple PDFs")
+    st.write("Upload PDF files and chat with them.")
+
+    # File uploader
+    uploaded_files = st.file_uploader("Upload PDF Files", accept_multiple_files=True, type=["pdf"])
+
+    if uploaded_files:
+        # Load PDF documents
+        documents = []
+        for uploaded_file in uploaded_files:
+            pdf_content = BytesIO(uploaded_file.read())
+            doc = fitz.open(stream=pdf_content, filetype="pdf")  # Open PDF with PyMuPDF
+            text = ""
+            for page in doc:
+                text += page.get_text()  # Extract text from each page
+            doc.close()
+
+            # Create Document instance with page content
+            documents.append(Document(page_content=text, metadata={"file_name": uploaded_file.name}))
+
+        # Split documents into manageable chunks
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+        chunks = text_splitter.split_documents(documents)
+
+        # Embed document chunks into vector store
+        vector_store = FAISS.from_documents(chunks, embeddings)
+
+        # User query input
+        st.write("You can now start chatting with your PDFs!")
+        user_input = st.text_input("Ask a question:")
+
+        if user_input:
+            # Perform similarity search on the vector store
+            docs = vector_store.similarity_search(user_input, k=3)
+
+            # Concatenate retrieved docs into a single prompt
+            prompt = "\n".join([doc.page_content for doc in docs]) + "\n\n" + user_input
+
+            # Generate response
+            try:
+                response = generator(prompt, max_new_tokens=50, num_return_sequences=1)[0]["generated_text"]
+                st.write(response)
+            except torch.cuda.OutOfMemoryError:
+                st.error("Out of memory. Try using a smaller model or fewer documents.")
+
+if __name__ == "__main__":
+    main()