Update app.py

app.py

@@ -1,30 +1,36 @@
 import gradio as gr
-from deepface import DeepFace
-import traceback
+import torch
+from torchvision import transforms
+from PIL import Image
+from transformers import AutoModelForImageClassification, AutoImageProcessor
 
-def detect_emotion(img):
-    try:
-        # DeepFace handles preprocessing and detection; enforce_detection=False avoids crashes on non-perfect faces
-        result = DeepFace.analyze(img, actions=['emotion'], enforce_detection=False)
-        emotion = result.get('dominant_emotion', None)
-        scores = result.get('emotion', {})
-        lines = []
-        if emotion:
-            lines.append(f"Dominant emotion: {emotion}")
-        if scores:
-            for k, v in sorted(scores.items(), key=lambda x: -x[1]):
-                lines.append(f"{k}: {v:.2f}")
-        return "\\n".join(lines) if lines else "No result"
-    except Exception as e:
-        return "Error during analysis:\\n" + str(e) + "\\n" + traceback.format_exc()
+model_name = "nateraw/fer-2013"
 
-iface = gr.Interface(
-    fn=detect_emotion,
-    inputs=gr.Image(type="numpy", label="Upload face image"),
-    outputs=gr.Textbox(label="Emotion Output"),
-    title="Emotion Detection (DeepFace)",
-    description="Detect facial emotions using DeepFace pretrained models."
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+])
+
+def predict(img):
+    img = Image.fromarray(img).convert("RGB")
+    inputs = processor(images=img, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    prob = logits.softmax(dim=1)
+    score, label_id = torch.max(prob, dim=1)
+    label = model.config.id2label[label_id.item()]
+    return f"Emotion: {label} ({score.item():.2f})"
+
+ui = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs="text",
+    title="Emotion Detection (PyTorch)",
+    description="Detect emotions using a lightweight PyTorch model (FER-2013)."
 )
 
-if __name__ == "__main__":
-    iface.launch()
+ui.launch()