Update app.py

app.py

@@ -1,24 +1,28 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import torch
+from PIL import Image
 
-# Load emotion detection model
-emotion_pipe = pipeline("image-classification", model="dima806/facial_emotions_image_detection")
+# Load model and processor
+processor = AutoImageProcessor.from_pretrained("dima806/facial_emotions_image_detection")
+model = AutoModelForImageClassification.from_pretrained("dima806/facial_emotions_image_detection")
 
+# Function to predict emotion
 def detect_emotion(image):
-    results = emotion_pipe(image)
-    if results:
-        top = results[0]
-        return {
-            "top_prediction": f"{top['label']} ({100*top['score']:.1f}%)",
-            "all_predictions": results
-        }
-    return {"top_prediction": "No face detected", "all_predictions": []}
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = logits.argmax(-1).item()
+    return model.config.id2label[predicted_class]
 
-# Create Gradio interface
-gr.Interface(
+# Gradio interface
+iface = gr.Interface(
     fn=detect_emotion,
     inputs=gr.Image(type="pil"),
-    outputs="json",  # Changed from "text" to "json"
-    title="High Accuracy Emotion Detector",
-    description="Powered by dima806/facial_emotions_image_detection (~91% accurate)"
-).launch()
+    outputs="text",
+    title="Facial Emotion Detection",
+    description="Upload a face image to detect emotion using dima806 model"
+)
+
+iface.launch(share=True)