Update gri.py

gri.py

@@ -1,42 +1,42 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from deep_translator import GoogleTranslator
-from langdetect import detect
-import torch
-
-MODEL_DIR = "model"
-tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR)
-emotion_labels = {
-    0: "Negative 😕",
-    1: "Neutral 😐",
-    2: "Positive 🙂"
-}
-translator = GoogleTranslator(source='auto', target='en')
-
-def predict_emotion(text):
-    detected_language = detect(text)
-    if detected_language != 'en':
-        translated_text = translator.translate(text)
-    else:
-        translated_text = text
-    inputs = tokenizer(translated_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    logits = outputs.logits
-    predicted_class = torch.argmax(logits, dim=-1).item()
-    emotion = emotion_labels.get(predicted_class, "Unknown")
-    return emotion
-
-iface = gr.Interface(
-    fn=predict_emotion,
-    inputs=gr.Textbox(lines=2, placeholder="Enter text here...", label="Input Text"),
-    outputs=[
-        gr.Textbox(label="Predicted Sentiment")
-    ],
-    title="Emotion Detection App",
-    description="Enter text in any language. The app will detect the language, translate if needed, and predict the emotion."
-)
-
-if __name__ == "__main__":
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from deep_translator import GoogleTranslator
+from langdetect import detect
+import torch
+
+MODEL_DIR = "model"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR, from_safetensors=True)
+emotion_labels = {
+    0: "Negative 😕",
+    1: "Neutral 😐",
+    2: "Positive 🙂"
+}
+translator = GoogleTranslator(source='auto', target='en')
+
+def predict_emotion(text):
+    detected_language = detect(text)
+    if detected_language != 'en':
+        translated_text = translator.translate(text)
+    else:
+        translated_text = text
+    inputs = tokenizer(translated_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=-1).item()
+    emotion = emotion_labels.get(predicted_class, "Unknown")
+    return emotion
+
+iface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text here...", label="Input Text"),
+    outputs=[
+        gr.Textbox(label="Predicted Sentiment")
+    ],
+    title="Emotion Detection App",
+    description="Enter text in any language. The app will detect the language, translate if needed, and predict the emotion."
+)
+
+if __name__ == "__main__":
     iface.launch(share = True)  # Set share=True to allow public access