Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+
+# List of your models (using repo IDs for easy loading)
+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",  # Adjust label if truncated
+    "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",  # Adjust label if truncated
+    "climate-d-s": "climatebert/distilroberta-base-climate-d-s",  # Adjust label if truncated
+    "climate-d": "climatebert/distilroberta-base-climate-d"   # Adjust label if truncated
+}
+
+# Cache for loaded pipelines (lazy loading)
+pipelines = {}
+
+def load_model(model_key):
+    """Load pipeline for the selected model (text-classification assumed; adjust task if needed)."""
+    if model_key not in pipelines:
+        repo_id = MODELS[model_key]
+        device = 0 if torch.cuda.is_available() else -1  # GPU if available
+        pipelines[model_key] = pipeline(
+            "text-classification",
+            model=repo_id,
+            device=device,
+            torch_dtype=torch.float16 if device == 0 else None  # Half-precision on GPU for memory savings
+        )
+    return pipelines[model_key]
+
+def predict(model_key, text):
+    """Run inference on selected model."""
+    if not text.strip():
+        return "Please enter some text."
+    
+    try:
+        model = load_model(model_key)
+        results = model(text)
+        # Format output (e.g., for classification: label + score)
+        formatted = "\n".join([f"{r['label']}: {r['score']:.2f}" for r in results])
+        return f"Predictions for '{text}':\n{formatted}"
+    except Exception as e:
+        return f"Error: {str(e)} (Check model card for task/input format)."
+
+# 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)