Update app.py

app.py

@@ -7,24 +7,14 @@ from huggingface_hub import from_pretrained_fastai
 repo_id = "jojimene/entregable3"
 learn = from_pretrained_fastai(repo_id)
 
-# Define the class mapping
-class_mapping = {
-    'negative': '0 risk',
-    'neutral': '1 neutral',
-    'positive': '2 opportunity'
-}
-
 # Define the prediction function
 def predict_sentiment(text):
     # Make prediction using the loaded model
     pred, _, probs = learn.predict(text)
-    # Map the predicted label to the desired format
-    mapped_pred = class_mapping.get(pred, pred)  # Use original if not in mapping
     # Get probabilities for each class, ensuring string keys
     labels = [str(label) for label in learn.dls.vocab[1]]  # Convert labels to strings
-    mapped_labels = [class_mapping.get(label, label) for label in labels]  # Map labels
-    result = {mapped_label: float(prob) for mapped_label, prob in zip(mapped_labels, probs)}
-    return {"predicted_sentiment": mapped_pred, "probabilities": result}
+    result = {label: float(prob) for label, prob in zip(labels, probs)}
+    return {"predicted_sentiment": pred, "probabilities": result}
 
 # Create Gradio interface
 iface = gr.Interface(
@@ -32,7 +22,7 @@ iface = gr.Interface(
     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 0 risk, 1 neutral, or 2 opportunity.",
+    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.",