app.py

from transformers import pipeline
import gradio as gr

# Initialize the pipeline
pipe = pipeline("text-classification", model="himanshutitoria/quick-commerce-perishability-model")

# Prediction function
def predict(text):
    result = pipe(text)
    return result[0]["label"]

# Define the interface
with gr.Blocks() as iface:
    # Add an image at the top
    gr.Image(
        value="top_image.webp",  # Replace with your image URL or file path
        label="Perishability Tagging",
        show_label=False,
    )

    input_textbox = gr.Textbox(
        label="Enter a Product Name",
        placeholder="Enter a name here...",
        interactive=True,
    )
    
    # Add the main Gradio interface
    with gr.Row():
        gr.Markdown("# Quick Commerce Perishability Tagging")
   
    outputs = gr.Textbox(label="Predicted result")
    gr.Interface(
        fn=predict,
        inputs=input_textbox,
        outputs=outputs,
        examples=[
            ["Milk"],
            ["Perfume"],
            ["Refined oil"],
            ["Tata Salt"],
            ["Cheese"],
            ["Butterscotch Icecream"],
            ["Thepla"],
        ],
    )
iface.launch()