Create app.py

app.py

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+import streamlit as st
+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 for secure access
+load_dotenv()
+
+# Configure the settings for Llama index using HuggingFace API
+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"
+)
+
+# Define directories for persistent storage and data handling
+PERSIST_DIR = "./db"
+DATA_DIR = "data"
+
+# Create directories if they do not exist
+os.makedirs(DATA_DIR, exist_ok=True)
+os.makedirs(PERSIST_DIR, exist_ok=True)
+
+def display_pdf(file_path):
+    """Display a PDF file in the Streamlit app."""
+    with open(file_path, "rb") as file:
+        base64_pdf = base64.b64encode(file.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)
+
+def ingest_data():
+    """Load and index documents from the data directory."""
+    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 process_query(query):
+    """Handle user queries by searching the indexed documents."""
+    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
+    index = load_index_from_storage(storage_context)
+    chat_text_qa_msgs = [
+    (
+        "user",
+        f"""You are a Q&A assistant named CHATTO, created by Prateek Mohan. You have a specific response programmed for when users specifically ask about your creator, Prateek Mohan. The response is: "I was created by Prateek Mohan, 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, Prateek 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:
+        return "Sorry, I couldn't find an answer."
+
+# Initialize the Streamlit app
+st.title("Chat with your PDF")
+st.markdown("Built by [Prateek Mohan]")
+
+
+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())
+            # display_pdf(filepath)  # Optionally display the uploaded PDF
+            ingest_data()  # 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 = process_query(user_prompt)
+    st.session_state.messages.append({'role': 'assistant', "content": response})
+
+for message in st.session_state.messages:
+    with st.chat_message(message['role']):
+        st.write(message['content'])