Update app.py

app.py

@@ -1,23 +1,55 @@
-import gradio as gr
-from transformers import pipeline
-
-# Load your Hugging Face model using transformers pipeline for image classification
-classifier = pipeline("image-classification", model="anismizi/skin-type-classifier")
-
-def analyze_skin(image):
-    # Run inference
-    results = classifier(image)
-    # Format results for display or API response
-    return {res['label']: float(res['score']) for res in results}
-
-# Create Gradio Interface
-iface = gr.Interface(
-    fn=analyze_skin,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(num_top_classes=2),
-    title="Skin Condition Analyzer",
-    description="Classify skin as dry or oily from image."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import gradio as gr
+from PIL import Image
+import torch
+from torchvision import transforms
+from transformers import AutoModelForImageClassification
+
+# Load the model (no AutoImageProcessor since it is unsupported)
+model_name = "anismizi/skin-type-classifier"
+model = AutoModelForImageClassification.from_pretrained(model_name)
+model.eval()  # Set model to eval mode
+
+# Define manual preprocessing transforms similar to ResNet50 training
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225]),
+])
+
+# Labels according to model info
+labels = ["dry", "oily"]
+
+def analyze_skin(image: Image.Image):
+    # Convert input image to RGB
+    image = image.convert("RGB")
+    # Preprocess image
+    input_tensor = preprocess(image)
+    # Add batch dimension
+    input_batch = input_tensor.unsqueeze(0)
+
+    # Run inference
+    with torch.no_grad():
+        outputs = model(input_batch)
+        logits = outputs.logits
+        probabilities = torch.nn.functional.softmax(logits, dim=1)
+        confidence, predicted_idx = torch.max(probabilities, dim=1)
+    
+    predicted_label = labels[predicted_idx.item()]
+    confidence_score = confidence.item()
+
+    # Format results for display and API output
+    return {predicted_label: confidence_score}
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=analyze_skin,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=2),  
+    title="Skin Condition Analyzer",
+    description="Classify skin as dry or oily from an image."
+)
+
+if __name__ == "__main__":
+    iface.launch()