app.py

import gradio as gr
from transformers import pipeline
import torch

# List of models
MODELS = {
    "econbert": "climatebert/econbert",
    "controversy-classification": "climatebert/ClimateControversyBERT_classification",
    "controversy-bert": "climatebert/ClimateControversyBert",
    "netzero-reduction": "climatebert/netzero-reduction",
    "transition-physical": "climatebert/transition-physical",
    "renewable": "climatebert/renewable",
    "climate-detector": "climatebert/distilroberta-base-climate-detector",
    "climate-commitment": "climatebert/distilroberta-base-climate-commitment",
    "climate-tcfd": "climatebert/distilroberta-base-climate-tcfd",
    "climate-s": "climatebert/distilroberta-base-climate-s",
    "climate-specificity": "climatebert/distilroberta-base-climate-specificity",
    "climate-sentiment": "climatebert/distilroberta-base-climate-sentiment",
    "environmental-claims": "climatebert/environmental-claims",
    "climate-f": "climatebert/distilroberta-base-climate-f",
    "climate-d-s": "climatebert/distilroberta-base-climate-d-s",
    "climate-d": "climatebert/distilroberta-base-climate-d"
}

# Human-readable label mappings
LABEL_MAPS = {
    "climate-commitment": {
        "LABEL_0": "Not about climate commitments",
        "LABEL_1": "About climate commitments",
    },
    "climate-detector": {
        "LABEL_0": "Not climate-related",
        "LABEL_1": "Climate-related",
    },
    "climate-sentiment": {
        "LABEL_0": "Negative",
        "LABEL_1": "Neutral",
        "LABEL_2": "Positive",
    },
    "climate-specificity": {
        "LABEL_0": "Low specificity",
        "LABEL_1": "Medium specificity",
        "LABEL_2": "High specificity",
    },
    "netzero-reduction": {
        "LABEL_0": "No net-zero / reduction commitment",
        "LABEL_1": "Net-zero / reduction commitment",
    },
    "transition-physical": {
        "LABEL_0": "Transition risk",
        "LABEL_1": "Physical risk",
    },
    "renewable": {
        "LABEL_0": "Not about renewables",
        "LABEL_1": "About renewables",
    },
    # You can expand mappings for other models after checking their model cards
}

# Cache for loaded pipelines
pipelines = {}

def load_model(model_key):
    """Load pipeline for the selected model with truncation enabled."""
    if model_key not in pipelines:
        repo_id = MODELS[model_key]
        device = 0 if torch.cuda.is_available() else -1
        pipelines[model_key] = pipeline(
            "text-classification",
            model=repo_id,
            device=device,
            torch_dtype=torch.float16 if device == 0 else None,
            truncation=True,
            max_length=512
        )
    return pipelines[model_key]

def predict(model_key, text):
    """Run inference on selected model with truncation and readable labels."""
    if not text.strip():
        return "Please enter some text."
    
    try:
        model = load_model(model_key)
        results = model(text)

        label_map = LABEL_MAPS.get(model_key, {})
        formatted = "\n".join([
            f"{label_map.get(r['label'], r['label'])}: {r['score']:.2f}"
            for r in results
        ])

        return f"Predictions for '{text[:50]}...':\n{formatted}"
    except Exception as e:
        return f"Error: {str(e)} (Check input length or model card for details)."

# Gradio interface
with gr.Blocks(title="ClimateBERT Multi-Model Demo") as demo:
    gr.Markdown("# 🌍 ClimateBERT Models Demo\nSelect a model and input text for climate-related analysis (e.g., sentiment, classification).")
    
    with gr.Row():
        model_dropdown = gr.Dropdown(
            choices=list(MODELS.keys()),
            label="Select Model",
            value=list(MODELS.keys())[0]
        )
        text_input = gr.Textbox(
            label="Input Text",
            placeholder="E.g., 'Companies must reduce emissions to net zero by 2050.'",
            lines=2
        )
    
    output = gr.Textbox(label="Output", lines=5)
    
    predict_btn = gr.Button("Run Inference")
    predict_btn.click(predict, inputs=[model_dropdown, text_input], outputs=output)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)