[Edit]

app.py

@@ -8,8 +8,11 @@ import gradio as gr
 from starlette.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles
 from gradio.routes import mount_gradio_app
+import tempfile
+import os
+from typing import Optional
 
-# Class names
+# Class names for skin disease classification
 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',
@@ -21,7 +24,7 @@ class_names = [
     'Warts Molluscum and other Viral Infections'
 ]
 
-# Load model and processor
+# Load the ConvNeXt 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"))
@@ -29,17 +32,19 @@ model.eval()
 
 processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224")
 
-# FastAPI app
+# FastAPI app setup
 app = FastAPI()
+
+# CORS Middleware to allow cross-origin requests
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  # Adjust for production
+    allow_origins=["*"],  # Allow all origins for demo purposes
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-# Predict function
+# Function to predict the skin disease from an image
 def predict(image: Image.Image):
     inputs = processor(images=image, return_tensors="pt")
     with torch.no_grad():
@@ -47,9 +52,10 @@ def predict(image: Image.Image):
         predicted_class = torch.argmax(outputs.logits, dim=1).item()
     return predicted_class, class_names[predicted_class]
 
-# FastAPI route
-@app.post("/predict/")
-async def predict_endpoint(file: UploadFile = File(...)):
+# FastAPI route for prediction via API
+@app.post("/api/predict")
+async def predict_from_upload(file: UploadFile = File(...)):
+    """API endpoint for image uploads"""
     try:
         img_bytes = await file.read()
         img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
@@ -61,15 +67,18 @@ async def predict_endpoint(file: UploadFile = File(...)):
     except Exception as e:
         return JSONResponse(content={"error": str(e)}, status_code=500)
 
+# Redirect root to Gradio interface
 @app.get("/")
 def redirect_root_to_gradio():
     return RedirectResponse(url="/gradio")
 
-# Gradio interface
+# Gradio interface for testing
 def gradio_interface(image):
+    """Gradio function to handle the prediction from image"""
     predicted_class, predicted_name = predict(image)
     return f"{predicted_name} (Class {predicted_class})"
 
+# Gradio app setup
 gradio_app = gr.Interface(
     fn=gradio_interface,
     inputs=gr.Image(type="pil"),
@@ -78,5 +87,10 @@ gradio_app = gr.Interface(
     description="Upload a skin image to classify the condition using a fine-tuned ConvNeXt model."
 )
 
-# Mount Gradio in FastAPI
+# Mount Gradio app into FastAPI
 app = mount_gradio_app(app, gradio_app, path="/gradio")
+
+# For running the app locally with uvicorn
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -8,4 +8,5 @@ Pillow
 python-multipart
 gradio
 transformers
+starlette