app.py

import gradio as gr
from transformers import pipeline

# def classify(text):
#     classifier = pipeline("zero-shot-classification")
#     candidate_labels = ["positive", "negative", "neutral"]
#     output = classifier(text, candidate_labels)
#     return output

# def classify(text):
#     classifier = pipeline("zero-shot-classification")
#     candidate_labels = ["positive", "negative", "neutral"]
#     output = classifier(text, candidate_labels)
#     output_labels = [label['label'] for label in output['labels']]
#     output_scores = [score for score in output['scores']]
#     sorted_output = sorted(zip(output_labels, output_scores), key=lambda x: x[1], reverse=True)
#     return sorted_output[:3]

# demo = gr.Interface(fn=classify,
#                     inputs=gr.Textbox(label="Enter text to classify"),
#                     outputs=gr.Label(num_top_classes=3))
    

# demo.launch()



classifier = pipeline("zero-shot-classification")

def classify(text):
    candidate_labels = ["positive", "negative", "neutral"]
    output = classifier(text, candidate_labels)
    # Process the output to match Gradio's expected input format for gr.Label
    labels = output['labels']
    scores = output['scores']
    # Construct a simple string representation of top classifications
    top_classes = ', '.join([f"{labels[i]}: {scores[i]:.2f}" for i in range(len(labels))])
    return top_classes

demo = gr.Interface(fn=classify,
                    inputs=gr.Textbox(label="Enter something"),
                    outputs=gr.Label())

demo.launch()