app.py

import torch
import torch.nn as nn
from torchvision.models import resnet50
from torchvision import transforms
from PIL import Image
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
import io

# ----------------------------------
# App
# ----------------------------------
app = FastAPI(title="Crop Disease Classification API")

# ----------------------------------
# Labels
# ----------------------------------
CLASS_LABELS = [
    'Corn_Common_Rust', 'Corn_Gray_Leaf_Spot', 'Corn_Healthy', 'Corn_Northern_Leaf_Blight',
    'Potato_Early_Blight', 'Potato_Healthy', 'Potato_Late_Blight',
    'Rice_Brown_Spot', 'Rice_Healthy', 'Rice_Leaf_Blast', 'Rice_Neck_Blast',
    'Wheat_Brown_Rust', 'Wheat_Healthy', 'Wheat_Yellow_Rust',
    'Sugarcane_Red_Rot', 'Sugarcane_Healthy', 'Sugarcane_Bacterial_Blight'
]

NUM_CLASSES = len(CLASS_LABELS)

# ----------------------------------
# Model
# ----------------------------------
class ResNetPlantDisease(nn.Module):
    def __init__(self, num_classes=17):
        super().__init__()
        self.backbone = resnet50(weights=None)
        self.backbone.fc = nn.Sequential(
            nn.Dropout(0.5),
            nn.Linear(2048, 512),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(512, num_classes)
        )

    def forward(self, x):
        return self.backbone(x)

model = ResNetPlantDisease(NUM_CLASSES)
model.load_state_dict(torch.load("plant_disease_resnet_model.pth", map_location="cpu"))
model.eval()

# ----------------------------------
# Transform
# ----------------------------------
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
])

# ----------------------------------
# API Endpoint
# ----------------------------------
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    try:
        image_bytes = await file.read()
        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
        img = transform(image).unsqueeze(0)

        with torch.no_grad():
            outputs = model(img)
            probs = torch.softmax(outputs, dim=1)[0]

        best_idx = torch.argmax(probs).item()
        confidence = float(probs[best_idx])

        if confidence < 0.6:
            return JSONResponse({
                "message": "Please upload a clearer leaf image"
            })

        return {
            "model": "ResNet50 Classification",
            "disease": CLASS_LABELS[best_idx],
            "confidence": round(confidence, 4)
        }

    except Exception as e:
        return JSONResponse({"error": str(e)})

import gradio as gr
import uvicorn
import threading

def run_api():
    uvicorn.run(app, host="0.0.0.0", port=8000)

threading.Thread(target=run_api).start()

gr.Markdown("""
# 🌾 Crop Disease Classification API

### Endpoint:
POST `/predict`

Use Postman / frontend to send image.
""").launch(server_name="0.0.0.0", server_port=7860)