fastapi

app.py

@@ -1,15 +1,29 @@
-import tempfile
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
 import tensorflow as tf
 import numpy as np
 from PIL import Image
-import gradio as gr
-import requests
-from io import BytesIO
+import io
+import uvicorn
+import tempfile
 import cv2
 
-# Load your model (you'll need to upload trained_modela.keras to your space)
+# Initialize FastAPI app
+app = FastAPI(title="Plant Disease Detection API", version="1.0.0")
+
+# Add CORS middleware to allow requests from your frontend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # In production, replace with your frontend URL
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load your model
 model = tf.keras.models.load_model('trained_modela.keras')
 
+# Define your class names (update with your actual classes)
 class_name = ['Apple___Apple_scab',
     'Apple___Black_rot',
     'Apple___Cedar_apple_rust',
@@ -50,22 +64,35 @@ class_name = ['Apple___Apple_scab',
     'Tomato___healthy']
 
 
+@app.get("/")
+async def root():
+    return {"message": "Plant Disease Detection API", "version": "1.0.0"}
 
-def predict_disease(image):
+@app.post("/predict")
+async def predict_disease(file: UploadFile = File(...)):
     """
-    Predict plant disease from uploaded image using same preprocessing as your working cv2 method
+    Predict plant disease from uploaded image
     """
     try:
+        # Validate file type
+        # Validate file type
+        if not file.content_type.startswith('image/'):
+            raise HTTPException(status_code=400, detail="File must be an image")
+
+        # Save uploaded file temporarily
         with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
             temp_path = tmp.name
-            image.save(temp_path) 
-            
+            contents = await file.read()
+            tmp.write(contents)
+        
+        # Read image using OpenCV
         img = cv2.imread(temp_path)
+        if img is None:
+            raise HTTPException(status_code=400, detail="Invalid image file")
         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        # Convert PIL image to array like load_img method
-        #image = image.convert("RGB")  # Ensure 3 channels
+        
         image = tf.keras.preprocessing.image.load_img(temp_path,target_size=(128, 128))
-        #image = image.resize((128, 128))  # Resize
+        
         input_arr = tf.keras.preprocessing.image.img_to_array(image)
         input_arr = np.array([input_arr])  # Convert single image to batch
 
@@ -75,22 +102,25 @@ def predict_disease(image):
         confidence = prediction[0][result_index]
         disease_name = class_name[result_index]
 
-        return f"Disease: {disease_name}\nConfidence: {confidence:.2%}"
+        return {
+            "success": True,
+            "disease": disease_name,
+            "confidence": confidence
+        }
 
+    except HTTPException as he:
+        raise he
     except Exception as e:
-        return f"Error: {str(e)}"
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
 
-# Create Gradio interface
-iface = gr.Interface(
-    fn=predict_disease,
-    inputs=gr.Image(type="pil", label="Upload Plant Image"),
-    outputs=gr.Textbox(label="Prediction Result"),
-    title="Plant Disease Detection API",
-    description="Upload an image of a plant leaf to detect diseases",
-    examples=[
-        # You can add example images here
-    ]
-)
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+@app.get("/classes")
+async def get_classes():
+    """Get all available disease classes"""
+    return {"classes": class_name}
 
 if __name__ == "__main__":
-    iface.launch()
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

@@ -3,4 +3,6 @@ numpy>=1.26.0,<2.2.0
 gradio == 5.44.1
 pillow == 10.4.0
 requests == 2.32.3
-opencv-python-headless == 4.12.0.88
+opencv-python-headless == 4.12.0.88
+fastapi==0.116.1
+uvicorn==0.35.0