app.py

import gradio as gr
import torch
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests

# Load a pre-trained Vision Transformer model from Hugging Face
model_name = "nateraw/vit-base-patch16-224-in21k"  # Replace with the model you've trained or a similar model
model = ViTForImageClassification.from_pretrained(model_name)
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)

# Define the disease labels (placeholders)
labels = {
    0: "Healthy",
    1: "Tobacco Mosaic Virus",
    2: "Brown Spot",
    3: "Frog Eye Leaf Spot",
    4: "Other"
}

# Define a function for disease detection
def detect_disease(image):
    # Preprocess the image
    inputs = feature_extractor(images=image, return_tensors="pt")
    
    # Run the model
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predicted_class = logits.argmax().item()
    
    # Get disease name from label dictionary
    disease_name = labels.get(predicted_class, "Unknown Disease")
    
    return f"Disease Detected: {disease_name}"

# Build Gradio Interface
title = "Tobacco Leaf Disease Detection"
description = """
Upload or take a real-time picture of a tobacco leaf, and the app will detect the disease (if any).
"""

# Create Gradio interface with camera and real-time processing
iface = gr.Interface(
    fn=detect_disease,
    inputs=gr.Image(source="camera", type="pil", tool="editor"),
    outputs="text",
    title=title,
    description=description,
    live=True  # Enables real-time processing
)

# Launch Gradio app
iface.launch()