app.py

import gradio as gr
import torch
import numpy as np
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
from PIL import Image
import requests
from io import BytesIO

# Load model and feature extractor
model_name = "Team-SknAI/SknAI-v4-10Labels"  # Replace with your model name
model = AutoModelForImageClassification.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)

def predict_image(image):
    model.eval()

    # Convert NumPy array to PIL image if needed
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)

    # Ensure the image is resized to match the expected input size
    image = image.resize((224, 224))

    inputs = feature_extractor(images=image, return_tensors="pt")
    
    # Ensure input matches model precision
    inputs = {k: v.to(model.dtype).to("cpu") for k, v in inputs.items()}  # Force CPU to avoid device mismatch

    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=-1).item()
    class_name = model.config.id2label[predicted_class]
    return class_name

app = gr.Interface(fn=predict_image, inputs="image", outputs="text")
app.launch(share=True)