Update app.py

app.py

@@ -69,7 +69,7 @@ file_extractor = {
 
 # Embedding model and index initialization (to be populated by uploaded files)
 # embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")   ## Works good
-embed_model1 = HuggingFaceEmbedding(model_name="BAAI/bge-large-en")    ## works good
+embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-large-en")    ## works good
 # embed_model2 = HuggingFaceEmbedding(model_name="NeuML/pubmedbert-base-embeddings") ## works good
 
 # sentence-transformers/distilbert-base-nli-mean-tokens
@@ -133,18 +133,18 @@ def respond(message, history):
 with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column(scale=1):
-            file_input = gr.File(file_count="single", type='filepath')
+            file_input = gr.File(file_count="single", type='filepath', label="Step-1: Upload document")
             with gr.Row():
                 clear = gr.ClearButton()
                 btn = gr.Button("Submit", variant='primary')
             output = gr.Text(label='Vector Index')
-            model_dropdown = gr.Dropdown(models, label="Select Model", interactive=True)
+            model_dropdown = gr.Dropdown(models, label="Step-2: Select Model", interactive=True)
 
         with gr.Column(scale=3):
             gr.ChatInterface(
                 fn=respond,
                 chatbot=gr.Chatbot(height=500),
-                textbox=gr.Textbox(placeholder="Ask me questions on the uploaded document!", container=False, scale=7)
+                textbox=gr.Textbox(placeholder="Step-3: Ask me questions on the uploaded document!", container=False, scale=7)
             )
 
     # Set up Gradio interactions