Create app.py

app.py

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+import os
+import streamlit as st
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.llms import HuggingFaceHub
+from langchain.schema import Document
+import requests
+from io import BytesIO
+import fitz  # PyMuPDF
+from dotenv import load_dotenv
+
+# Set device based on GPU availability
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Hugging Face API token should now be loaded from the .env file
+# Explicitly set the Hugging Face API token from the environment variable
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACE_API_TOKEN")
+
+# Load embeddings with Hugging Face API
+embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
+embeddings = HuggingFaceEmbeddings(model_name=embedding_model)  # Removed api_key parameter
+
+# Set up the text generation model using Hugging Face Hub
+model_name = "google/flan-t5-small"  # Use a smaller model to reduce response time and cost
+llm = HuggingFaceHub(repo_id=model_name, huggingfacehub_api_token=os.getenv("HUGGINGFACEHUB_API_TOKEN"), model_kwargs={"max_length": 256, "temperature": 0.7})
+
+# 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 using the Hugging Face API
+            try:
+                response = llm(prompt)
+                st.write(response)
+            except requests.exceptions.RequestException as e:
+                st.error(f"Error connecting to Hugging Face API: {e}")
+
+if __name__ == "__main__":
+    main()