Update app.py

app.py

@@ -5,52 +5,35 @@ 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]
+# Model path on Hugging Face Hub
+model_path = "HaryaniAnjali/Llama_3.2_Trained_Emotion"
+
+# Load the tokenizer with error handling
+try:
+    print("Loading tokenizer...")
+    tokenizer = AutoTokenizer.from_pretrained(model_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
+    print("Tokenizer loaded successfully.")
+except Exception as e:
+    print(f"Error loading tokenizer: {e}")
+
+# Load the model with error handling
+try:
+    print("Loading model...")
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_path, num_labels=7, ignore_mismatched_sizes=True, torch_dtype=torch.float16
+    ).to(device)
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"Error loading model: {e}")
 
 # 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."
+def predict_emotion(text):
+    if model is None or tokenizer is None:
+        return "Model or tokenizer failed to load."
 
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=256).to(device)
     
@@ -64,10 +47,10 @@ def predict_emotion(text, model_name):
 # Gradio UI
 ui = gr.Interface(
     fn=predict_emotion,
-    inputs=["text", gr.Radio(list(model_paths.keys()), label="Select Model")],
+    inputs="text",
     outputs="text",
     title="Emotion Classifier",
-    description="Enter a text, select a model, and classify its emotion."
+    description="Enter a text and classify its emotion."
 )
 
-ui.queue().launch()
+ui.launch()