app.py

# import sklearn
from os import O_ACCMODE
import gradio as gr
import joblib
from transformers import pipeline
import requests.exceptions
from huggingface_hub import HfApi, hf_hub_download
from huggingface_hub.repocard import metadata_load


app = gr.Blocks()

model_id_1 = "nlptown/bert-base-multilingual-uncased-sentiment"
model_id_2 = "microsoft/deberta-base"
model_id_3 = "distilbert-base-uncased-finetuned-sst-2-english"
model_id_4 = "lordtt13/emo-mobilebert"
model_id_5 = "juliensimon/reviews-sentiment-analysis"


def load_agent(model_id):
    """
    This function load the agent's results
    """
    # Load the metrics
    metadata = get_metadata(model_id)
    # get predictions
    predictions = predict(model_id)

    return model_id, predictions


def get_metadata(model_id):
    """
    Get the metadata of the model repo
    :param model_id:
    :return: metadata
    """
    try:
        readme_path = hf_hub_download(model_id, filename="README.md")
        metadata = metadata_load(readme_path)
        print(metadata)
        return metadata
    except requests.exceptions.HTTPError:
        return None

def get_prediction(model_id):

    classifier = pipeline("text-classification", model=model_id, return_all_scores=True)
             
    def predict(review):
            prediction = classifier(review)
            print(prediction)
            return prediction
    return predict


with app:
    gr.Markdown(
    """
    # Compare Sentiment Analysis Models 
    
    Type text to predict sentiment.
    """)   
    with gr.Row():
        inp_1= gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.")

    gr.Markdown(
    """
    **Model Predictions**
    """)

    gr.Markdown(
    """
    Model 1 = nlptown/bert-base-multilingual-uncased-sentiment
    """)

    with gr.Row():
        btn1 = gr.Button("Predict - Model 1")
    with gr.Row():
        out_1 = gr.Textbox(label="Predictions for Model 1")
    btn1.click(fn=get_prediction(model_id_1), inputs=inp_1, outputs=out_1)

    gr.Markdown(
    """
    Model 2 = microsoft/deberta-base
    """)

    with gr.Row():
        btn2 = gr.Button("Predict - Model 2")
    with gr.Row():
        out_2 = gr.Textbox(label="Predictions for Model 2")      
    btn2.click(fn=get_prediction(model_id_2), inputs=inp_1, outputs=out_2)

    gr.Markdown(
    """
    Model 3 = distilbert-base-uncased-finetuned-sst-2-english"
    """)

    with gr.Row():
        btn3 = gr.Button("Predict - Model 3")
    with gr.Row():
        out_3 = gr.Textbox(label="Predictions for Model 3")      
    btn3.click(fn=get_prediction(model_id_3), inputs=inp_1, outputs=out_3)

    gr.Markdown(
    """
    Model 4 = lordtt13/emo-mobilebert
    """)

    with gr.Row():
        btn4 = gr.Button("Predict - Model 4")
    with gr.Row():
        out_4 = gr.Textbox(label="Predictions for Model 4")      
    btn4.click(fn=get_prediction(model_id_4), inputs=inp_1, outputs=out_4)

    gr.Markdown(
    """
    Model 5 = juliensimon/reviews-sentiment-analysis
    """)

    with gr.Row():
        btn5 = gr.Button("Predict - Model 5")
    with gr.Row():
        out_5 = gr.Textbox(label="Predictions for Model 5")      
    btn5.click(fn=get_prediction(model_id_5), inputs=inp_1, outputs=out_5)

   
app.launch()