Update app.py
Browse files
app.py
CHANGED
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import streamlit as st
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import os
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import base64
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from huggingface_hub import InferenceApi
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from dotenv import load_dotenv
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from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from llama_index.core import Settings
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# Load environment variables
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load_dotenv()
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#
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#
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# Define the directory for persistent storage and data
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PERSIST_DIR = "./db"
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@@ -25,38 +33,48 @@ DATA_DIR = "data"
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os.makedirs(DATA_DIR, exist_ok=True)
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os.makedirs(PERSIST_DIR, exist_ok=True)
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# Function to display the PDF file in Streamlit
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def displayPDF(file):
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with open(file, "rb") as f:
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base64_pdf = base64.b64encode(f.read()).decode('utf-8')
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pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
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st.markdown(pdf_display, unsafe_allow_html=True)
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# Function to process data ingestion
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def data_ingestion():
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documents = SimpleDirectoryReader(DATA_DIR).load_data()
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storage_context = StorageContext.from_defaults()
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index = VectorStoreIndex.from_documents(documents)
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index.storage_context.persist(persist_dir=PERSIST_DIR)
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try:
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return response
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except Exception as e:
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return f"An error occurred: {str(e)}"
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# Streamlit app initialization
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st.title("Chat with your PDF π")
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st.markdown("Built by [vivek](https://github.com/saravivek-cyber)")
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st.markdown("Chat here")
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# Initial message setup
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if 'messages' not in st.session_state:
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st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about its content.'}]
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# Sidebar for file upload and processing
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with st.sidebar:
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st.title("Menu:")
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uploaded_file = st.file_uploader("Upload your PDF File")
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data_ingestion()
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st.success("Data ingestion completed.")
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# Handling user input for querying the PDF content
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user_prompt = st.chat_input("Ask me anything about the content of the PDF:")
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if user_prompt:
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st.session_state.messages.append({'role': 'user', "content": user_prompt})
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response =
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st.session_state.messages.append({'role': 'assistant', "content": response})
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# Displaying chat messages
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for message in st.session_state.messages:
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with st.chat_message(message['role']):
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st.write(message['content'])
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import streamlit as st
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from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
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from llama_index.llms.huggingface import HuggingFaceInferenceAPI
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from dotenv import load_dotenv
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from llama_index.core import Settings
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import os
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import base64
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# Load environment variables
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load_dotenv()
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# Configure the Llama index settings for using Hugging Face LLaMA model
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Settings.llm = HuggingFaceInferenceAPI(
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model_name="facebook/bedrock-llama-7b", # Use LLaMA 7B model here
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tokenizer_name="facebook/bedrock-llama-7b", # Tokenizer for the LLaMA model
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context_window=30000, # Set context window size (adjust if necessary)
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api_token=os.getenv("HF_TOKEN"), # Hugging Face API Token
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max_new_tokens=512,
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generate_kwargs={"temperature": 0.1}, # Control the generation temperature
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)
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# Set up Hugging Face Embedding model to use powerful LLaMA model
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Settings.embed_model = HuggingFaceEmbedding(
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model_name="facebook/bedrock-llama-7b" # Powerful model for embeddings
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)
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# Define the directory for persistent storage and data
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PERSIST_DIR = "./db"
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os.makedirs(DATA_DIR, exist_ok=True)
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os.makedirs(PERSIST_DIR, exist_ok=True)
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def displayPDF(file):
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with open(file, "rb") as f:
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base64_pdf = base64.b64encode(f.read()).decode('utf-8')
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pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
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st.markdown(pdf_display, unsafe_allow_html=True)
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def data_ingestion():
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documents = SimpleDirectoryReader(DATA_DIR).load_data()
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storage_context = StorageContext.from_defaults()
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index = VectorStoreIndex.from_documents(documents)
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index.storage_context.persist(persist_dir=PERSIST_DIR)
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def handle_query(query):
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storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
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index = load_index_from_storage(storage_context)
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chat_text_qa_msgs = [
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(
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"user",
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"""created by vivek created for Neonflake Enterprises OPC Pvt Ltd
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Context:
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{context}
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Question:
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{query}
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"""
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)
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]
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text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
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query_engine = index.as_query_engine(text_qa_template=text_qa_template)
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try:
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answer = query_engine.query({"context": "Extracted context from PDF", "query": query})
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return answer.get('response', "Sorry, no answer found.")
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except Exception as e:
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return f"An error occurred: {str(e)}"
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# Streamlit app initialization
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st.title("Chat with your PDF π")
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st.markdown("Built by [vivek](https://github.com/saravivek-cyber)")
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if 'messages' not in st.session_state:
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st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about its content.'}]
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with st.sidebar:
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st.title("Menu:")
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uploaded_file = st.file_uploader("Upload your PDF File")
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data_ingestion()
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st.success("Data ingestion completed.")
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user_prompt = st.chat_input("Ask me anything about the content of the PDF:")
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if user_prompt:
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st.session_state.messages.append({'role': 'user', "content": user_prompt})
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response = handle_query(user_prompt)
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st.session_state.messages.append({'role': 'assistant', "content": response})
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for message in st.session_state.messages:
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with st.chat_message(message['role']):
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st.write(message['content'])
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