Update app.py

app.py

@@ -1,27 +1,17 @@
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
+import tempfile
 import tensorflow as tf
 import numpy as np
 from PIL import Image
-import io
-import uvicorn
-import tempfile
+import gradio as gr
+import requests
+from io import BytesIO
 import cv2
 
-# 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=["*"],
-)
+model = tf.keras.models.load_model('trained_modela.keras')
 
 # 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',
@@ -63,32 +53,20 @@ class_name = ['Apple___Apple_scab',
     'Tomato___Tomato_mosaic_virus',
     'Tomato___healthy']
 
-
-@app.get("/")
-async def root():
-    return {"message": "Plant Disease Detection API", "version": "1.0.0"}
-
-@app.post("/predict")
-async def predict_disease(file: UploadFile = File(...)):
+def predict_disease(image):
     """
-    Predict plant disease from uploaded image
+    Predict plant disease from uploaded image using same preprocessing as your working cv2 method
     """
     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
-            contents = await file.read()
-            tmp.write(contents)
+            image.save(temp_path) 
+           
         
         # 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)
         
         image = tf.keras.preprocessing.image.load_img(temp_path,target_size=(128, 128))
@@ -102,25 +80,22 @@ async def predict_disease(file: UploadFile = File(...)):
         confidence = prediction[0][result_index]
         disease_name = class_name[result_index]
 
-        return {
-            "success": True,
-            "disease": disease_name,
-            "confidence": confidence
-        }
+        return f"Disease: {disease_name}\nConfidence: {confidence:.2%}"
 
-    except HTTPException as he:
-        raise he
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
+        return f"Error: {str(e)}"
 
-@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}
+# 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
+    ]
+)
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    iface.launch()