Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from transformers import BertForSequenceClassification, BertTokenizer
+import requests
+import json
+
+# Load model and tokenizer from Hugging Face Hub
+repo_id = "logasanjeev/goemotions-bert"
+model = BertForSequenceClassification.from_pretrained(repo_id)
+tokenizer = BertTokenizer.from_pretrained(repo_id)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+if torch.cuda.device_count() > 1:
+    model = nn.DataParallel(model)
+model.eval()
+
+# Load optimized thresholds from Hugging Face Hub
+thresholds_url = f"https://huggingface.co/{repo_id}/raw/main/thresholds.json"
+response = requests.get(thresholds_url)
+thresholds_data = json.loads(response.text)
+emotion_labels = thresholds_data["emotion_labels"]
+best_thresholds = thresholds_data["thresholds"]
+
+# Prediction function
+def predict_emotions(text):
+    encodings = tokenizer(
+        text,
+        padding='max_length',
+        truncation=True,
+        max_length=128,
+        return_tensors='pt'
+    )
+    input_ids = encodings['input_ids'].to(device)
+    attention_mask = encodings['attention_mask'].to(device)
+    
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask=attention_mask)
+        logits = torch.sigmoid(outputs.logits).cpu().numpy()[0]
+    
+    predictions = []
+    for i, (logit, thresh) in enumerate(zip(logits, best_thresholds)):
+        if logit >= thresh:
+            predictions.append((emotion_labels[i], logit))
+    
+    predictions.sort(key=lambda x: x[1], reverse=True)
+    if not predictions:
+        return "No emotions predicted above thresholds."
+    
+    return "\n".join([f"{emotion}: {confidence:.4f}" for emotion, confidence in predictions])
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict_emotions,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your text here..."),
+    outputs="text",
+    title="GoEmotions BERT Classifier",
+    description="Predict emotions using a fine-tuned BERT-base model from logasanjeev/goemotions-bert.",
+    examples=[
+        "I’m just chilling today.",
+        "Thank you for saving my life!",
+        "I’m nervous about my exam tomorrow."
+    ]
+)
+
+if __name__ == "__main__":
+    interface.launch()