Edit

app.py

@@ -1,19 +1,23 @@
 import torch
 from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import JSONResponse
+from fastapi.responses import JSONResponse, RedirectResponse
 from transformers import ConvNextForImageClassification, AutoImageProcessor
 from PIL import Image
 import io
+import gradio as gr
+from starlette.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from gradio.routes import mount_gradio_app
 
-# Class names (for skin diseases)
+# Class names
 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', 
+    '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'
 ]
 
@@ -27,8 +31,15 @@ processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224")
 
 # FastAPI app
 app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Adjust for production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-# Prediction helper
+# Predict function
 def predict(image: Image.Image):
     inputs = processor(images=image, return_tensors="pt")
     with torch.no_grad():
@@ -36,7 +47,7 @@ def predict(image: Image.Image):
         predicted_class = torch.argmax(outputs.logits, dim=1).item()
     return predicted_class, class_names[predicted_class]
 
-# Endpoint: /predict
+# FastAPI route
 @app.post("/predict/")
 async def predict_endpoint(file: UploadFile = File(...)):
     try:
@@ -50,6 +61,22 @@ async def predict_endpoint(file: UploadFile = File(...)):
     except Exception as e:
         return JSONResponse(content={"error": str(e)}, status_code=500)
 
-# Required for Hugging Face Spaces (do NOT run uvicorn manually)
-# Just expose the app
-app = app
+@app.get("/")
+def redirect_root_to_gradio():
+    return RedirectResponse(url="/gradio")
+
+# Gradio interface
+def gradio_interface(image):
+    predicted_class, predicted_name = predict(image)
+    return f"{predicted_name} (Class {predicted_class})"
+
+gradio_app = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Skin Disease Classifier",
+    description="Upload a skin image to classify the condition using a fine-tuned ConvNeXt model."
+)
+
+# Mount Gradio in FastAPI
+app = mount_gradio_app(app, gradio_app, path="/gradio")