Update app.py

app.py

@@ -1,102 +1,102 @@
-    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
-    import altair as alt
-
-    # Load environment variables
-    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 the directory for persistent storage and data
-    PERSIST_DIR = "./db"
-    DATA_DIR = "data"
-
-    # Ensure data directory exists
-    os.makedirs(DATA_DIR, exist_ok=True)
-    os.makedirs(PERSIST_DIR, exist_ok=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)
-
-    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",
-            """created by vivek created for Neonflake Enterprises OPC Pvt Ltd
-            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 app initialization
-    st.title("Chat with your PDF📄")
-    st.markdown("Built by [vivek](https://github.com/saravivek-cyber)")
-    st.markdown("chat here")
-
-    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'])
+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
+import altair as alt
+
+# Load environment variables
+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 the directory for persistent storage and data
+PERSIST_DIR = "./db"
+DATA_DIR = "data"
+
+# Ensure data directory exists
+os.makedirs(DATA_DIR, exist_ok=True)
+os.makedirs(PERSIST_DIR, exist_ok=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)
+
+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",
+        """created by vivek created for Neonflake Enterprises OPC Pvt Ltd
+        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 app initialization
+st.title("Chat with your PDF📄")
+st.markdown("Built by [vivek](https://github.com/saravivek-cyber)")
+st.markdown("chat here")
+
+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'])