File size: 2,444 Bytes
0875b04 16db478 0875b04 0152dd4 0875b04 0152dd4 0875b04 0152dd4 0875b04 cc9935d e041ecb 0875b04 0152dd4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
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 Recognition",
description="Enter a text to recognize its emotion."
)
ui.queue().launch()
|