| import gradio as gr |
| import torch |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
|
|
| |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
| |
| model_paths = { |
| "LLaMA-3.2": "HaryaniAnjali/Llama_3.2_Trained_Emotion" |
| } |
|
|
| |
| tokenizers = {} |
| for name, path in model_paths.items(): |
| try: |
| print(f"🔄 Loading tokenizer for {name}...") |
| tokenizer = AutoTokenizer.from_pretrained(path) |
|
|
| |
| if tokenizer.pad_token is None: |
| tokenizer.pad_token = tokenizer.eos_token |
| |
| tokenizers[name] = tokenizer |
| print(f"Tokenizer loaded for {name}") |
| except Exception as e: |
| print(f"Error loading tokenizer for {name}: {e}") |
|
|
| |
| 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] |
|
|
| |
| 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] |
|
|
| |
| ui = gr.Interface( |
| fn=predict_emotion, |
| inputs=["text", gr.Radio(list(model_paths.keys()), label="Select Model")], |
| outputs="text", |
| title="Emotion Recognition", |
| description="Enter a text to recognize its emotion." |
| ) |
|
|
| ui.queue().launch() |
|
|
