added classification route for plant-disease-detection

main.py

@@ -1,7 +1,10 @@
 from fastapi import FastAPI, File, UploadFile, Request, Form
 from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
+from transformers import pipeline
 import uvicorn
+from PIL import Image
+import io
 import numpy as np
 from projects.DL_CatDog.DL_CatDog import preprocess_image, read_image, model_DL_CatDog
 from projects.ML_StudentPerformance.ML_StudentPerformace import predict_student_performance, create_custom_data, form1
@@ -23,7 +26,7 @@ app.add_middleware(
 def home():
     return {"message": "FastAPI server is running on Hugging Face Spaces!"}
 
-# # Prediction route for DL_CatDog
+# Prediction route for DL_CatDog
 @app.post("/api/predict1")
 async def predict_DL_CatDog(file: UploadFile = File(...)):
     try:
@@ -35,6 +38,22 @@ async def predict_DL_CatDog(file: UploadFile = File(...)):
     except Exception as e:
         return JSONResponse(content={"ok": -1, "message": f"Something went wrong! {str(e)}"}, status_code=500)
 
+# Classification route for DL_PlantDisease
+pipe = pipeline("image-classification", model="wambugu71/crop_leaf_diseases_vit")
+@app.post("/api/classify")
+async def classify_image(file: UploadFile = File(...)):
+    try:
+        # Read the uploaded image file
+        image = Image.open(io.BytesIO(await file.read()))
+
+        # Run the image through the Hugging Face model
+        predictions = pipe(image)
+
+        return JSONResponse(content={"ok": 1, "predictions": predictions})
+    except Exception as e:
+        return JSONResponse(content={"ok": -1, "message": f"Something went wrong! {str(e)}"}, status_code=500)
+
+
 # Prediction route for ML_StudentPerformance
 @app.post("/api/predict2")
 async def predict_student_performance_api(request: form1):