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import torch
from fastapi import FastAPI, File, UploadFile
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
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()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Adjust for production
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Predict function
def predict(image: Image.Image):
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
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(...)):
try:
img_bytes = await file.read()
img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
predicted_class, predicted_name = predict(img)
return JSONResponse(content={
"predicted_class": predicted_class,
"predicted_name": predicted_name
})
except Exception as e:
return JSONResponse(content={"error": str(e)}, status_code=500)
@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")
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