import gradio as gr from transformers import pipeline import os from dotenv import load_dotenv load_dotenv() token = os.environ.get("HF_TOKEN") raw_model_name = "distilbert-base-uncased" raw_model = pipeline("sentiment-analysis", model=raw_model_name) scademy_500_500_model_name = "scademy/DistilBert-500-500-0" scademy_500_500_model = pipeline("sentiment-analysis", model=scademy_500_500_model_name, token=token) scademy_2500_2500_model_name = "scademy/DistilBERT-2500-2500-0" scademy_2500_2500_model = pipeline("sentiment-analysis", model=scademy_2500_2500_model_name, token=token) scademy_12500_12500_model_name = "scademy/DistilBERT-12500-12500-0" scademy_12500_12500_model = pipeline("sentiment-analysis", model=scademy_12500_12500_model_name, token=token) fine_tuned_model_name = "distilbert-base-uncased-finetuned-sst-2-english" fine_tuned_model = pipeline("sentiment-analysis", model=fine_tuned_model_name) def get_model_output(input_text, model_choice): raw_result = raw_model(input_text) scademy_500_500_result = scademy_500_500_model(input_text) scademy_2500_2500_result = scademy_2500_2500_model(input_text) scademy_12500_12500_result = scademy_12500_12500_model(input_text) fine_tuned_result = fine_tuned_model(input_text) return ( format_model_output(raw_result[0]), format_model_output(scademy_500_500_result[0]), format_model_output(scademy_2500_2500_result[0]), format_model_output(scademy_12500_12500_result[0]), format_model_output(fine_tuned_result[0]) ) def format_model_output(output): return f"I am {output['score']*100:.2f}% sure that the sentiment is {output['label']}" iface = gr.Interface( fn=get_model_output, title="DistilBERT Sentiment Analysis", inputs=[ gr.Textbox(label="Input Text"), ], outputs=[ gr.Textbox(label="Base DistilBERT output (distilbert-base-uncased)"), gr.Textbox(label="Scademy DistilBERT 500-500 output"), gr.Textbox(label="Scademy DistilBERT 2500-2500 output"), gr.Textbox(label="Scademy DistilBERT 12500-12500 output"), gr.Textbox(label="Fine-tuned DistilBERT output"), ], ) iface.launch()