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app.py

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+import torch
+import gradio as gr
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+from transformers import ConvNextForImageClassification, AutoImageProcessor
+from PIL import Image
+import io
+
+# Class names (for skin diseases)
+class_names = [
+    'Acne and Rosacea Photos', 'Actinic Keratosis Basal Cell Carcinoma and other Malignant Lesions', 'Atopic Dermatitis Photos',
+    'Bullous Disease Photos', 'Cellulitis Impetigo and other Bacterial Infections', 'Eczema Photos', 'Exanthems and Drug Eruptions', 
+    'Hair Loss Photos Alopecia and other Hair Diseases', 'Herpes HPV and other STDs Photos', 'Light Diseases and Disorders of Pigmentation', 
+    'Lupus and other Connective Tissue diseases', 'Melanoma Skin Cancer Nevi and Moles', 'Nail Fungus and other Nail Disease', 
+    'Poison Ivy Photos and other Contact Dermatitis', 'Psoriasis pictures Lichen Planus and related diseases', 
+    'Scabies Lyme Disease and other Infestations and Bites', 'Seborrheic Keratoses and other Benign Tumors', 'Systemic Disease', 
+    'Tinea Ringworm Candidiasis and other Fungal Infections', 'Urticaria Hives', 'Vascular Tumors', 'Vasculitis Photos', 
+    'Warts Molluscum and other Viral Infections'
+]
+
+# Load model and processor
+model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-224")
+model.classifier = torch.nn.Linear(in_features=1024, out_features=23)
+model.load_state_dict(torch.load("./models/convnext_base_finetuned.pth", map_location="cpu"))
+model.eval()
+
+processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224")
+
+# FastAPI app
+app = FastAPI()
+
+# Helper function for processing the image
+def predict(image: Image.Image):
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_class = torch.argmax(outputs.logits, dim=1).item()
+    
+    return predicted_class, class_names[predicted_class]
+
+# FastAPI endpoint to handle image upload and prediction
+@app.post("/predict/")
+async def predict_endpoint(file: UploadFile = File(...)):
+    try:
+        # Read and process the image
+        img_bytes = await file.read()
+        img = Image.open(io.BytesIO(img_bytes))
+        
+        # Get the prediction
+        predicted_class, predicted_name = predict(img)
+        
+        # Return the result as JSON
+        return JSONResponse(content={"predicted_class": predicted_class, "predicted_name": predicted_name})
+    
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)
+
+# Gradio function to integrate with the FastAPI prediction
+def gradio_predict(image: Image.Image):
+    predicted_class, predicted_name = predict(image)
+    return f"Predicted Class: {predicted_name}"
+
+# Gradio Interface
+iface = gr.Interface(fn=gradio_predict, inputs=gr.Image(type="pil"), outputs=gr.Textbox())
+
+# Serve Gradio interface on FastAPI
+@app.get("/gradio/")
+async def gradio_interface():
+    return iface.launch(share=True, inline=True)
+
+# Run the FastAPI app using Uvicorn
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

models/convnext_base_finetuned.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c8dadf0c017fd3749a0dc291a1d9249bdba618c6351964d1fe65a85c07a578b
+size 350500802

requirements.txt

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+fastapi
+uvicorn
+torch
+torchvision
+opencv-python
+numpy
+Pillow
+python-multipart
+gradio
+transformers
+