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README.md

@@ -1,12 +1,13 @@
 ---
-title: PDF Llama
-emoji: 📉
-colorFrom: green
-colorTo: yellow
+title: LLama PDF
+emoji: 🏃
+colorFrom: purple
+colorTo: blue
 sdk: streamlit
-sdk_version: 1.27.2
+sdk_version: 1.27.1
 app_file: app.py
 pinned: false
+license: llama2
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# Import necessary modules for processing documents, embeddings, Q&A, etc. from 'langchain' library.
+from dotenv import load_dotenv
+load_dotenv()  # Load environment variables from a .env file.
+from langchain.document_loaders import PyPDFLoader  # For loading and reading PDF documents.
+from langchain.text_splitter import RecursiveCharacterTextSplitter  # For splitting large texts into smaller chunks.
+from langchain.vectorstores import Chroma  # Vector storage system for embeddings.
+from langchain.llms import CTransformers  # For loading transformer models.
+# from InstructorEmbedding import INSTRUCTOR  # Not clear without context, possibly a custom embedding.
+from langchain.embeddings import HuggingFaceInstructEmbeddings  # Embeddings from HuggingFace models with instructions.
+from langchain.embeddings import HuggingFaceEmbeddings  # General embeddings from HuggingFace models.
+from langchain.embeddings import LlamaCppEmbeddings  # Embeddings using the Llama model.
+from langchain.chains import RetrievalQA  # Q&A retrieval system.
+from langchain.embeddings import OpenAIEmbeddings  # Embeddings from OpenAI models.
+from langchain.vectorstores import FAISS  # Another vector storage system for embeddings.
+
+# Import Streamlit for creating a web application and other necessary modules for file handling.
+import streamlit as st  # Main library for creating the web application.
+import tempfile  # For creating temporary directories and files.
+import os  # For handling file and directory paths.
+
+# Import a handler for streaming outputs.
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler  # For live updates in the Streamlit app.
+
+st.title("ChatPDF")
+
+st.markdown("""
+ChatPDF is a web application that can answer questions based on a PDF document. To use the app, simply upload a PDF file and type your question in the input box. The app will then use a powerful language model to generate an answer to your question.
+""")
+
+# Create a visual separator in the app.
+st.write("---")
+
+# Add a file uploader widget for users to upload their PDF files.
+uploaded_file = st.sidebar.file_uploader("Upload your PDF file!", type=['pdf'])
+# Another visual separator after the file uploader.
+st.write("---")
+
+# Function to convert the uploaded PDF into a readable document format.
+def pdf_to_document(uploaded_file):
+    # Create a temporary directory for storing the uploaded PDF.
+    temp_dir = tempfile.TemporaryDirectory()
+    # Get the path where the uploaded PDF will be stored temporarily.
+    temp_filepath = os.path.join(temp_dir.name, uploaded_file.name)
+    
+    # Save the uploaded PDF to the temporary path.
+    with open(temp_filepath, "wb") as f:
+        f.write(uploaded_file.getvalue())
+    
+    # Load the PDF and split it into individual pages.
+    loader = PyPDFLoader(temp_filepath)
+    pages = loader.load_and_split()
+    return pages
+
+# Check if a user has uploaded a file.
+if uploaded_file is not None:
+    # Convert the uploaded PDF into a document format.
+    pages = pdf_to_document(uploaded_file)
+
+    # Initialize a tool to split the document into smaller textual chunks.
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size = 300,  # Define the size of each chunk.
+        chunk_overlap  = 20,  # Define how much chunks can overlap.
+        length_function = len  # Function to determine the length of texts.
+    )
+    # Split the document into chunks.
+    texts = text_splitter.split_documents(pages)
+
+    ## Below are examples of different embedding techniques, but they are commented out.
+
+    # Load the desired embeddings model.
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
+                                       model_kwargs={'device': 'cpu'})
+
+    # Load the textual chunks into the Chroma vector store.
+    db = Chroma.from_documents(texts, embeddings)
+
+    # Custom handler to stream outputs live to the Streamlit application.
+    from langchain.callbacks.base import BaseCallbackHandler
+    class StreamHandler(BaseCallbackHandler):
+        def __init__(self, container, initial_text=""):
+            self.container = container  # Streamlit container to display text.
+            self.text=initial_text
+        def on_llm_new_token(self, token: str, **kwargs) -> None:
+            self.text+=token  # Add new tokens to the text.
+            self.container.markdown(self.text)  # Display the text.
+
+    # Header for the Q&A section of the web app.
+    st.header("Ask the PDF a question!")
+    # Input box for users to type their questions.
+    question = st.text_input('Type your question')
+
+    # Check if the user has pressed the 'Ask' button.
+    if st.button('Ask'):
+        # Display a spinner while processing the question.
+        with st.spinner('Processing...'):
+            # Space to display the answer.
+            chat_box = st.empty()
+            # Initialize the handler to stream outputs.
+            stream_hander = StreamHandler(chat_box)
+
+            # Initialize the Q&A model and chain.
+            llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q2_K.bin", model_type="llama", callbacks=[stream_hander])
+            qa_chain = RetrievalQA.from_chain_type(llm, retriever=db.as_retriever())
+            # Get the answer to the user's question.
+            qa_chain({"query": question})

requirements.txt

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+langchain
+pypdf
+chromadb
+tiktoken
+pysqlite3-binary
+streamlit-extras
+InstructorEmbedding
+sentence-transformers