Delete app.py

app.py

@@ -1,126 +0,0 @@
-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 docx2txt
-
-# Load environment variables
-load_dotenv()
-
-icons = {"assistant": "robot.png", "user": "man-kddi.png"}
-
-# Configure the Llama index settings
-Settings.llm = HuggingFaceInferenceAPI(
-    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    context_window=3900,
-    token=os.getenv("HF_TOKEN"),
-    max_new_tokens=1000,
-    generate_kwargs={"temperature": 0.5},
-)
-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 displayDOCX(file):
-    text = docx2txt.process(file)
-    st.text_area("Document Content", text, height=400)
-
-def displayTXT(file):
-    with open(file, "r") as f:
-        text = f.read()
-    st.text_area("Document Content", text, height=400)
-
-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 Q&A assistant named CHAT-DOC. 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."
-
-# Streamlit app initialization
-st.title("Chat with your Document 📄")
-st.markdown("Chat here👇")
-
-if 'messages' not in st.session_state:
-    st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF, DOCX, or TXT file and ask me anything about its content.'}]
-
-for message in st.session_state.messages:
-    with st.chat_message(message['role'], avatar=icons[message['role']]):
-        st.write(message['content'])
-
-with st.sidebar:
-    st.title("Menu:")
-    uploaded_file = st.file_uploader("Upload your document (PDF, DOCX, TXT)", type=["pdf", "docx", "txt"])
-    if st.button("Submit & Process") and uploaded_file:
-        with st.spinner("Processing..."):
-            file_extension = os.path.splitext(uploaded_file.name)[1].lower()
-            filepath = os.path.join(DATA_DIR, "uploaded_file" + file_extension)
-            with open(filepath, "wb") as f:
-                f.write(uploaded_file.getbuffer())
-            
-            if file_extension == ".pdf":
-                displayPDF(filepath)
-            elif file_extension == ".docx":
-                displayDOCX(filepath)
-            elif file_extension == ".txt":
-                displayTXT(filepath)
-            
-            data_ingestion()  # Process file every time a new file is uploaded
-            st.success("Done")
-
-user_prompt = st.chat_input("Ask me anything about the content of the document:")
-
-if user_prompt and uploaded_file:
-    st.session_state.messages.append({'role': 'user', "content": user_prompt})
-    with st.chat_message("user", avatar=icons["user"]):
-        st.write(user_prompt)
-
-    # Trigger assistant's response retrieval and update UI
-    with st.spinner("Thinking..."):
-        response = handle_query(user_prompt)
-        with st.chat_message("assistant", avatar=icons["assistant"]):
-            st.write(response)
-        st.session_state.messages.append({'role': 'assistant', "content": response})