Update app.py

app.py

@@ -11,3 +11,78 @@ def classify_text(text):
 # Create a Gradio interface
 interface = gr.Interface(fn=classify_text, inputs="text", outputs="json")
 interface.launch()
+
+
+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Setup device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Model paths on Hugging Face Hub
+model_paths = {
+    "LLaMA-3.2": "HaryaniAnjali/Llama_3.2_Trained_Emotion"
+}
+
+# Load tokenizers first with error handling
+tokenizers = {}
+for name, path in model_paths.items():
+    try:
+        print(f"🔄 Loading tokenizer for {name}...")
+        tokenizer = AutoTokenizer.from_pretrained(path)
+
+        # Ensure the tokenizer has a padding token
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token  # Use EOS as padding token if none exists
+        
+        tokenizers[name] = tokenizer
+        print(f"✅ Tokenizer loaded for {name}")
+    except Exception as e:
+        print(f"❌ Error loading tokenizer for {name}: {e}")
+
+# Lazy loading of models to save memory
+models = {}
+
+def get_model(model_name):
+    if model_name not in models:
+        try:
+            print(f"🔄 Loading model: {model_name}...")
+            models[model_name] = AutoModelForSequenceClassification.from_pretrained(
+                model_paths[model_name], num_labels=7, ignore_mismatched_sizes=True, torch_dtype=torch.float16
+            ).to(device)
+            print(f"✅ Model {model_name} loaded successfully.")
+        except Exception as e:
+            print(f"❌ Error loading {model_name}: {e}")
+            return None
+    return models[model_name]
+
+# Emotion classification function
+def predict_emotion(text, model_name):
+    model = get_model(model_name)
+    if model is None:
+        return f"❌ Model {model_name} failed to load. Check logs."
+
+    tokenizer = tokenizers.get(model_name)
+    if tokenizer is None:
+        return f"❌ Tokenizer for {model_name} not available."
+
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=256).to(device)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_label = torch.argmax(outputs.logits, dim=1).item()
+
+    labels = ["anger", "disgust", "fear", "guilt", "joy", "sadness", "shame"]
+    return labels[predicted_label]
+
+# Gradio UI
+ui = gr.Interface(
+    fn=predict_emotion,
+    inputs=["text", gr.Radio(list(model_paths.keys()), label="Select Model")],
+    outputs="text",
+    title="Emotion Classifier",
+    description="Enter a text, select a model, and classify its emotion."
+)
+
+ui.queue().launch()