Update app.py

app.py

@@ -1,115 +1,4 @@
-import gradio as gr
-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 tempfile
-
-# 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 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 EazyPeazy, 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."
-
-def process_file(file):
-    if file is None:
-        return "Please upload a PDF file."
-    
-    temp_dir = tempfile.mkdtemp()
-    temp_path = os.path.join(temp_dir, "uploaded.pdf")
-    
-    with open(temp_path, "wb") as f:
-        f.write(file.read())
-    
-    # Copy the file to the DATA_DIR
-    os.makedirs(DATA_DIR, exist_ok=True)
-    dest_path = os.path.join(DATA_DIR, "saved_pdf.pdf")
-    os.replace(temp_path, dest_path)
-    
-    # Process the uploaded PDF
-    data_ingestion()
-    
-    return "PDF processed successfully. You can now ask questions about its content."
-
-def chatbot(message, history):
-    response = handle_query(message)
-    history.append((message, response))
-    return history, ""
-
-# Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# (PDF) Information and Inference🗞️")
-    gr.Markdown("Retrieval-Augmented Generation")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            file_output = gr.Textbox(label="Upload Status")
-            upload_button = gr.UploadButton("Upload PDF", file_types=[".pdf"])
-            upload_button.upload(process_file, upload_button, file_output)
-        
-        with gr.Column(scale=2):
-            chatbot = gr.Chatbot(
-                [],
-                elem_id="chatbot",
-                bubble_full_width=False,
-            )
-            msg = gr.Textbox(label="Ask me anything about the content of the PDF:")
-            clear = gr.Button("Clear")
-    
-    msg.submit(chatbot, [msg, chatbot], [chatbot, msg])
-    clear.click(lambda: None, None, chatbot, queue=False)
+from gradio_app import demo
 
 if __name__ == "__main__":
     demo.launch()