app.py

from transformers import AutoTokenizer, AutoModel
import torch
import os
import gradio as gr

# Load Hugging Face Token
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    raise ValueError("❌ Hugging Face API token not found! Set HF_TOKEN as an environment variable.")

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("mental/mental-bert-base-uncased", use_auth_token=HF_TOKEN)
model = AutoModel.from_pretrained("mental/mental-bert-base-uncased", use_auth_token=HF_TOKEN,output_hidden_states=True)

model.eval()  # Set model to evaluation mode


    
def infer(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    
    last_hidden_state = outputs.last_hidden_state  # (1, seq_len, hidden_size)
    mask = inputs['attention_mask'].unsqueeze(-1).expand(last_hidden_state.size()).float()
    
    masked_embeddings = last_hidden_state * mask
    summed = torch.sum(masked_embeddings, dim=1)
    counts = torch.clamp(mask.sum(dim=1), min=1e-9)
    mean_pooled = summed / counts
    
    return mean_pooled.squeeze().tolist()


# Gradio interface
iface = gr.Interface(
    fn=infer,
    inputs=[
        gr.Textbox(label="text"),
    ],
    outputs="text"
)
iface.launch()