Update for all model running

app.py

@@ -1,8 +1,10 @@
 import gradio as gr
-from transformers import pipeline
 import torch
+from transformers import pipeline
 
-# List of models
+# -------------------
+# 1. Model definitions
+# -------------------
 MODELS = {
     "econbert": "climatebert/econbert",
     "controversy-classification": "climatebert/ClimateControversyBERT_classification",
@@ -19,10 +21,12 @@ MODELS = {
     "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"
+    "climate-d": "climatebert/distilroberta-base-climate-d",
 }
 
-# Human-readable label mappings
+# -------------------
+# 2. Human-readable label maps
+# -------------------
 LABEL_MAPS = {
     "climate-commitment": {
         "LABEL_0": "Not about climate commitments",
@@ -54,17 +58,19 @@ LABEL_MAPS = {
         "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
+# -------------------
+# 3. Pipeline cache
+# -------------------
 pipelines = {}
 
 def load_model(model_key):
-    """Load pipeline for the selected model with truncation enabled."""
+    """Load and cache a model pipeline."""
     if model_key not in pipelines:
         repo_id = MODELS[model_key]
         device = 0 if torch.cuda.is_available() else -1
+        print(f"🔹 Loading model: {model_key} ({repo_id})")
         pipelines[model_key] = pipeline(
             "text-classification",
             model=repo_id,
@@ -75,45 +81,57 @@ def load_model(model_key):
         )
     return pipelines[model_key]
 
-def predict(model_key, text):
-    """Run inference on selected model with truncation and readable labels."""
+# -------------------
+# 4. Inference across all models
+# -------------------
+def predict_all_models(text):
+    """Run inference across all ClimateBERT models and return structured output."""
     if not text.strip():
-        return "Please enter some text."
-    
-    try:
-        model = load_model(model_key)
-        results = model(text)
+        return "⚠️ Please enter some 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
-        ])
+    results_summary = []
+    for model_key, repo in MODELS.items():
+        try:
+            model = load_model(model_key)
+            outputs = model(text)
+            label_map = LABEL_MAPS.get(model_key, {})
 
-        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)."
+            formatted = "\n".join([
+                f"• {label_map.get(r['label'], r['label'])}: {r['score']:.2f}"
+                for r in outputs
+            ])
 
-# 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)
+            results_summary.append(f"### {model_key}\n{formatted}")
+        except Exception as e:
+            results_summary.append(f"### {model_key}\n❌ Error: {str(e)}")
+
+    return "\n\n".join(results_summary)
+
+# -------------------
+# 5. Gradio UI
+# -------------------
+with gr.Blocks(title="🌍 ClimateBERT All-Models Analyzer") as demo:
+    gr.Markdown("""
+    # 🌍 ClimateBERT Multi-Model Analysis
+    This app runs **all ClimateBERT models** on your input text (`mergedMarkdown` style).  
+    It detects sentiment, specificity, renewables, commitments, and more — all at once.
+    """)
+
+    text_input = gr.Textbox(
+        label="Input Text (mergedMarkdown)",
+        placeholder="Paste the sustainability report, ESG statement, or corporate disclosure here...",
+        lines=5
+    )
+
+    output = gr.Markdown(label="Model Outputs")
+
+    run_btn = gr.Button("🔍 Run All Models")
+    run_btn.click(predict_all_models, inputs=text_input, outputs=output)
+
+    gr.Markdown("""
+    ---
+    **Note:** Each model captures a different aspect of climate-related discourse (e.g., sentiment, specificity, commitments, etc.).
+    """)
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch(server_name="0.0.0.0", server_port=7860)