Update app.py

app.py

@@ -1,49 +1,114 @@
 import gradio as gr
-from llama_index import load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
+import pandas as pd
+import pickle
+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
 
-# Define directories
+# Load environment variables for HuggingFace API
+load_dotenv()
+
+# 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"
+)
+
+# Define directories for persistent storage and data
 PERSIST_DIR = "./db"
 DATA_DIR = "data"
 
-# Ensure directories exist
+# Ensure data directory exists
 os.makedirs(DATA_DIR, exist_ok=True)
 os.makedirs(PERSIST_DIR, exist_ok=True)
 
+# Function to display PDF in Streamlit
+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)
+
+# Function to handle PDF ingestion
 def data_ingestion():
-    try:
-        documents = SimpleDirectoryReader(DATA_DIR).load_data()
-        index = VectorStoreIndex.from_documents(documents)
-        index.storage_context.persist(persist_dir=PERSIST_DIR)  # Adjust this if storage is handled differently
-    except Exception as e:
-        print(f"Error during data ingestion: {str(e)}")
+    documents = SimpleDirectoryReader(DATA_DIR).load_data()
+    storage_context = StorageContext.from_defaults()
+    index = VectorStoreIndex.from_documents(documents)
+    index.storage_context.persist(persist_dir=PERSIST_DIR)
 
+# Function to handle user queries
 def handle_query(query):
-    try:
-        index = load_index_from_storage(persist_dir=PERSIST_DIR)  # Adjust loading method if needed
-        
-        chat_text_qa_msgs = [
-            (
-                "user",
-                """You are a Q&A assistant named AskAI, created by Vatsa. 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:
-            return "Sorry, I couldn't find an answer."
-    except Exception as e:
-        print(f"Error during query handling: {str(e)}")
-        return "An error occurred while processing your 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 AskAI, created by Vatsa. You have a specific response programmed for when users ask about your creator, Suriya. The response is: "I was created by Suriya, an enthusiast in Artificial Intelligence."
+        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."
+
+# Streamlit interface
+st.title("Integrated App: Gradio & Streamlit")
+st.markdown("### PDF Q&A + Machine Health Prediction")
+
+# Gradio App via iframe
+st.markdown("#### Machine Health Prediction App (Gradio)")
+gradio_app_url = "http://localhost:7860"  # URL where Gradio app is running
+st.markdown(f'<iframe src="{gradio_app_url}" width="100%" height="800"></iframe>', unsafe_allow_html=True)
+
+# Streamlit section for PDF upload and chat
+st.markdown("#### PDF Chatbot App (Streamlit)")
+
+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})
+
+for message in st.session_state.messages:
+    with st.chat_message(message['role']):
+        st.write(message['content'])
+