app.py

import gradio as gr
import pandas as pd
from fastai.text.all import *
from huggingface_hub import from_pretrained_fastai

# Load the model from Hugging Face Hub
repo_id = "jojimene/entregable3"
learn = from_pretrained_fastai(repo_id)

# Define the prediction function
def predict_sentiment(text):
    # Make prediction using the loaded model
    pred, _, probs = learn.predict(text)
    # Get probabilities for each class, ensuring string keys
    labels = [str(label) for label in learn.dls.vocab[1]]  # Convert labels to strings
    result = {label: float(prob) for label, prob in zip(labels, probs)}
    return {"predicted_sentiment": pred, "probabilities": result}

# Create Gradio interface
iface = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(lines=5, placeholder="Enter text for sentiment analysis..."),
    outputs=gr.JSON(),
    title="Climate Sentiment Classifier",
    description="Enter a text related to climate sentiment, and the model will predict whether it's positive, negative, or neutral.",
    examples=[
        "Renewable energy is the future of our planet!",
        "Climate change is a serious threat to humanity.",
        "The weather is nice today, but I'm worried about global warming."
    ],
    cache_examples=False  # Disable caching to avoid startup error
)

# Launch the interface
if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)