import sklearn
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

#pipe = joblib.load("https://huggingface.co/spaces/scikit-learn/sentiment-analysis/tree/main/pipeline.pkl")
#inputs = [gr.Textbox(value = "The customer service was satisfactory.")]
#outputs = [gr.Label(label = "Sentiment")]
#title = "Sentiment Analysis"

app = gr.Blocks()

def load_agent(model_id_1, model_id_2):
    """
    This function load the agent's results
    """
    # Load the metrics
    metadata_1 = get_metadata(model_id_1)
    
    # Get the accuracy
    results_1 = parse_metrics_accuracy(metadata_1)
    
    # Load the metrics
    metadata_2 = get_metadata(model_id_2)
    
    # Get the accuracy
    results_2 = parse_metrics_accuracy(metadata_2)

    return model_id_1, results_1, model_id_2, results_2

def parse_metrics_accuracy(meta):
    if "model-index" not in meta:
        return None
    result = meta["model-index"][0]["results"]
    metrics = result[0]["metrics"]
    accuracy = metrics[0]["value"]
    return accuracy

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

classifier = pipeline("text-classification", model="juliensimon/distilbert-amazon-shoe-reviews")
                
def predict(review):
        prediction = classifier(review)
        print(prediction)
        stars = prediction[0]['label']
        stars = (int)(stars.split('_')[1])+1
        score = 100*prediction[0]['score']
        return "{} {:.0f}%".format("\U00002B50"*stars, score)

with app:
    gr.Markdown(
    """
    # Compare Sentiment Analysis Models 
    
    Type text to predict sentiment.
    """)
    with gr.Row():
      model1_input = gr.Textbox(label="Model 1")
      model2_input = gr.Textbox(label="Model 2")
      
    with gr.Row():
        inp = gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.")
        out = gr.Textbox(label="Prediction")
    btn = gr.Button("Run")
    btn.click(fn=predict, inputs=inp, outputs=out)
    
    gr.Markdown(
    """
    Type two models id you want to compare or check examples below.
    """)
    with gr.Row():
      model1_input = gr.Textbox(label="Model 1")
      model2_input = gr.Textbox(label="Model 2") 
    with gr.Row():
      app_button = gr.Button("Compare models")
    with gr.Row():
      with gr.Column():
        model1_name = gr.Markdown()
        model1_score_output = gr.Textbox(label="Sentiment")
      with gr.Column():
        model2_name = gr.Markdown()
        model2_score_output = gr.Textbox(label="Sentiment")

    app_button.click(load_agent, inputs=[model1_input, model2_input], outputs=[model1_name, model1_score_output, model2_name, model2_score_output])
    
    examples = gr.Examples(examples=[["distilbert-base-uncased-finetuned-sst-2-english","distilbert-base-uncased-finetuned-sst-2-english"],
        ["distilbert-base-uncased-finetuned-sst-2-english", "distilbert-base-uncased-finetuned-sst-2-english"]],
                           inputs=[model1_input, model2_input])

    
app.launch()