Update app.py

app.py

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+from datetime import datetime
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_parse import LlamaParse
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+import os
+from dotenv import load_dotenv
+import gradio as gr
+
+# Load environment variables
+load_dotenv()
+
+# Initialize the LLM and parser
+llm = HuggingFaceInferenceAPI(
+    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
+    token=os.getenv("TOKEN")
+)
+
+parser = LlamaParse(api_key=os.getenv("LLAMA_INDEX_API"), result_type='markdown')
+file_extractor = {'.pdf': parser, '.docx': parser, '.doc': parser}
+
+# Embedding model and index initialization (to be populated by uploaded files)
+embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+
+# Global variable to store documents loaded from user-uploaded files
+vector_index = None
+
+# File processing function
+def load_files(file_path: str):
+    try:
+        global vector_index
+        document = SimpleDirectoryReader(input_files=[file_path], file_extractor=file_extractor).load_data()
+        vector_index = VectorStoreIndex.from_documents(document, embed_model=embed_model)
+        print(f"parsing done {file_path}")
+        filename = os.path.basename(file_path)
+        return f"Ready to give response on give {filename}"
+    except Exception as e:
+        return f"An error occurred {e}"
+
+def respond(message, history):
+    try:
+        query_engine = vector_index.as_query_engine(llm=llm)
+        bot_message = query_engine.query(message)
+        # output = ""
+        # for chr in bot_message:
+        #     output += chr
+        #     yield output
+        print(f"{datetime.now()}::message=>{str(bot_message)}")
+        return str(bot_message)
+    except Exception as e:
+        if e == "'NoneType' object has no attribute 'as_query_engine'":
+            return "upload file"
+        return f"an error occurred {e}"
+
+# UI Setup
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            file_input = gr.File(file_count="single", type='filepath')
+            with gr.Column():
+                clear = gr.ClearButton()
+                btn = gr.Button("Submit", variant='primary')
+            output = gr.Text(label='Vector Index')
+        with gr.Column(scale=2):
+             gr.ChatInterface(fn=respond,
+                              chatbot=gr.Chatbot(height=500),
+                              textbox=gr.Textbox(placeholder="Ask me a yes or no question", container=False, scale=7),
+                              examples=["summarize the document"]
+                             )
+
+    # Action on button click to process file and load into index
+    btn.click(fn=load_files, inputs=file_input, outputs=output)
+    clear.click(lambda: [None]*2, outputs=[file_input, output])
+
+
+# Launch the demo with public link option
+if __name__ == "__main__":
+    demo.launch(share=True)