Update app.py

app.py

@@ -2,12 +2,12 @@ from fastapi import FastAPI, File, UploadFile
 from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
 import uvicorn
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
-from PIL import Image
-from io import BytesIO
+# import numpy as np # <-- KOMENTARI ATAU HAPUS BARIS INI
+# import tensorflow as tf # <-- KOMENTARI ATAU HAPUS BARIS INI
+# from tensorflow.keras.models import load_model # <-- KOMENTARI ATAU HAPUS BARIS INI
+# from tensorflow.keras.preprocessing import image # <-- KOMENTARI ATAU HAPUS BARIS INI
+from PIL import Image # <-- TETAPKAN INI JIKA KAMU MASIH INGIN MENERIMA FILE
+from io import BytesIO # <-- TETAPKAN INI JIKA KAMU MASIH INGIN MENERIMA FILE
 
 # === Inisialisasi FastAPI ===
 app = FastAPI()
@@ -15,66 +15,45 @@ app = FastAPI()
 # === Middleware CORS agar bisa diakses dari frontend (JS) ===
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  # Ganti dengan domain frontend-mu untuk keamanan
+    allow_origins=["*"],
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-# === Konfigurasi Model ===
-MODEL_PATH = 'model_cnn.h5'  # Pastikan path model benar
-IMG_HEIGHT = 224
-IMG_WIDTH = 224
+# === Konfigurasi Model === (KOMENTARI ATAU HAPUS SELURUH BAGIAN INI)
+# MODEL_PATH = 'model_cnn.h5'
+# IMG_HEIGHT = 224
+# IMG_WIDTH = 224
+# class_names = [...]
+# model = load_model(MODEL_PATH)
 
-class_names = [
-    'freshapples', 'freshbanana', 'freshbittergroud', 'freshcapsicum', 'freshcucumber',
-    'freshokra', 'freshoranges', 'freshpotato', 'freshtomato',
-    'rottenapples', 'rottenbanana', 'rottenbittergroud', 'rottencapsicum', 'rottencucumber',
-    'rottenokra', 'rottenoranges', 'rottenpotato', 'rottentomato'
-]
-
-# Muat model sekali saja saat startup
-model = load_model(MODEL_PATH)
-
-# === Fungsi bantu ===
-def read_imagefile(file) -> Image.Image:
-    image = Image.open(BytesIO(file)).convert("RGB")  # Pastikan RGB
-    return image
-
-def predict(img: Image.Image):
-    # Resize dan ubah ke array tanpa normalisasi ulang (karena model sudah punya Rescaling)
-    img = img.resize((IMG_WIDTH, IMG_HEIGHT))
-    img_array = image.img_to_array(img)  # [0,255]
-    img_array = np.expand_dims(img_array, axis=0)  # [1, 224, 224, 3]
-
-    prediction = model.predict(img_array)
-    predicted_class = np.argmax(prediction[0])
-    confidence = float(prediction[0][predicted_class]) * 100
-
-    return class_names[predicted_class], confidence
+# === Fungsi bantu === (KOMENTARI ATAU HAPUS SELURUH BAGIAN INI)
+# def read_imagefile(file) -> Image.Image:
+#     image = Image.open(BytesIO(file)).convert("RGB")
+#     return image
+# def predict(img: Image.Image):
+#     # ... prediction logic ...
 
 # === Endpoint utama ===
 @app.post("/predict")
 async def predict_image(file: UploadFile = File(...)):
     try:
-        img = read_imagefile(await file.read())
-        pred_class, confidence = predict(img)
+        # Kita tidak akan memproses file, hanya menerima untuk demo
+        # await file.read() # <-- Bisa dikomentari juga jika tidak perlu baca filenya
+        # read_imagefile(await file.read()) # <-- Pastikan ini tidak dipanggil
 
         response = {
             "filename": file.filename,
-            "title": "Prediction Result",
-            "message": f"The item is classified as: {pred_class}",
-            "confidence": f"{confidence:.2f}",
-            "details": [f"Class: {pred_class}", f"Confidence: {confidence:.2f}%"]
+            "title": "Prediction Result (DUMMY)",
+            "message": "Ini adalah respons DUMMY. API berfungsi!",
+            "confidence": "99.99",
+            "details": ["Class: dummy_class", "Confidence: 99.99%"]
         }
 
         return JSONResponse(content=response)
     except Exception as e:
         return JSONResponse(content={"error": str(e)}, status_code=500)
 
-# === Untuk menjalankan secara lokal ===
 if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=False)
-
-# uvicorn app:app --reload
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=False)