app.py

import gradio as gr
from transformers import pipeline

#pipe = pipeline("text-classification", model="qualitydatalab/autotrain-car-review-project-966432121")

def predict(text):
    label2emoji = {"poor": "🙁", "ok": "😐", "great": "😊"}
    #preds = pipe(text)[0]
    return label2emoji["poor"], round(1, 5)
  
gradio_ui = gr.Interface(
    fn=predict,
    title="Predicting review scores from customer reviews on cars",
    description="Enter some review text about a car model and check what the model predicts for it's rating.",
    inputs=[
        gr.inputs.Textbox(lines=5, label="Paste some text here"),
    ],
    outputs=[
        gr.outputs.Textbox(label="Label"),
        gr.outputs.Textbox(label="Score"),
    ],
    examples=[
        [" Bought this Clio for my daughter 2 years ago.  It was very well cared for with 95,000 miles.  New brakes, tires, and all scheduled maintenance was done.  We have had no problems (ex a torn axle boot) in 2 years and 22,000 miles.  This is our favorite car (we have 4) as it has a great balance of economy, handling, comfort, and build quality."], 
        ["I bought a brand new Scenic for my daily driver last year, and this is my biggest mistake! I should of do more researches before buying it. This car is the MOST POS ever. My suspension was bad after a couple thousand miles, the car made huge noise when the car is rolling."],
    ],
    allow_flagging="never",
    analytics_enabled=False

)

gradio_ui.launch(server_port=8080, enable_queue=False)