Update app.py

app.py

@@ -1,53 +1,101 @@
 import streamlit as st
-from PyPDF2 import PdfReader
-from transformers import pipeline
-import os
+from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
+from llama_index.llms.huggingface import HuggingFaceInferenceAPI
 from dotenv import load_dotenv
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import Settings
+import os
+import base64
 
 # Load environment variables
 load_dotenv()
-os.getenv("HUGGINGFACE_TOKEN")
 
-# Initialize the Hugging Face pipeline for question-answering
-qa_pipeline = pipeline(
-    "question-answering",
-    model="deepset/roberta-base-squad2",
-    tokenizer="deepset/roberta-base-squad2",
+# Configure the Llama index settings
+Settings.llm = HuggingFaceInferenceAPI(
+    model_name="google/gemma-1.1-7b-it",
+    tokenizer_name="google/gemma-1.1-7b-it",
+    context_window=3000,
+    token=os.getenv("HF_TOKEN"),
+    max_new_tokens=512,
+    generate_kwargs={"temperature": 0.1},
+)
+Settings.embed_model = HuggingFaceEmbedding(
+    model_name="BAAI/bge-small-en-v1.5"
 )
 
-def get_pdf_text(pdf_docs):
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text() + "\n"
-    return text
+# Define the directory for persistent storage and data
+PERSIST_DIR = "./db"
+DATA_DIR = "data"
 
-def main():
-    st.set_page_config(page_title="Chat with PDF", page_icon="📄")
-    st.header("Chat with PDF using Hugging Face 🤖")
+# Ensure data directory exists
+os.makedirs(DATA_DIR, exist_ok=True)
+os.makedirs(PERSIST_DIR, exist_ok=True)
 
-    # Upload PDF files
-    pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True)
+def displayPDF(file):
+    with open(file, "rb") as f:
+        base64_pdf = base64.b64encode(f.read()).decode('utf-8')
+    pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
+    st.markdown(pdf_display, unsafe_allow_html=True)
 
-    if st.button("Submit & Process"):
-        if pdf_docs:
-            with st.spinner("Processing..."):
-                raw_text = get_pdf_text(pdf_docs)
-                st.session_state.raw_text = raw_text  # Store raw text in session state
-                st.success("PDF processed successfully!")
-        else:
-            st.warning("Please upload at least one PDF file.")
-
-    # User input for questions
-    if 'raw_text' in st.session_state:
-        user_question = st.text_input("Ask a Question from the PDF Files")
-
-        if user_question:
-            response = qa_pipeline(question=user_question, context=st.session_state.raw_text)
-            st.write("Answer:", response['answer'])
+def data_ingestion():
+    documents = SimpleDirectoryReader(DATA_DIR).load_data()
+    storage_context = StorageContext.from_defaults()
+    index = VectorStoreIndex.from_documents(documents)
+    index.storage_context.persist(persist_dir=PERSIST_DIR)
+
+def handle_query(query):
+    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
+    index = load_index_from_storage(storage_context)
+    chat_text_qa_msgs = [
+    (
+        "user",
+        """You are a Q&A assistant named CHATTO, created by Suriya. You have a specific response programmed for when users specifically ask about your creator, Suriya. The response is: "I was created by Suriya, an enthusiast in Artificial Intelligence. He is dedicated to solving complex problems and delivering innovative solutions. With a strong focus on machine learning, deep learning, Python, generative AI, NLP, and computer vision, Suriya is passionate about pushing the boundaries of AI to explore new possibilities." For all other inquiries, your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document.
+        Context:
+        {context_str}
+        Question:
+        {query_str}
+        """
+    )
+    ]
+    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
+    
+    query_engine = index.as_query_engine(text_qa_template=text_qa_template)
+    answer = query_engine.query(query)
+    
+    if hasattr(answer, 'response'):
+        return answer.response
+    elif isinstance(answer, dict) and 'response' in answer:
+        return answer['response']
     else:
-        st.info("Please upload PDF files to start chatting.")
+        return "Sorry, I couldn't find an answer."
+
+
+# Streamlit app initialization
+st.title("(PDF) Information and Inference🗞️")
+st.markdown("Retrieval-Augmented Generation") 
+st.markdown("start chat ...🚀")
+
+if 'messages' not in st.session_state:
+    st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about its content.'}]
+
+with st.sidebar:
+    st.title("Menu:")
+    uploaded_file = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button")
+    if st.button("Submit & Process"):
+        with st.spinner("Processing..."):
+            filepath = "data/saved_pdf.pdf"
+            with open(filepath, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+            # displayPDF(filepath)  # Display the uploaded PDF
+            data_ingestion()  # Process PDF every time new file is uploaded
+            st.success("Done")
+
+user_prompt = st.chat_input("Ask me anything about the content of the PDF:")
+if user_prompt:
+    st.session_state.messages.append({'role': 'user', "content": user_prompt})
+    response = handle_query(user_prompt)
+    st.session_state.messages.append({'role': 'assistant', "content": response})
 
-if __name__ == "__main__":
-    main()
+for message in st.session_state.messages:
+    with st.chat_message(message['role']):
+        st.write(message['content'])