update main.py dan update semua model terbaru

.gitattributes

@@ -36,3 +36,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 freshly_saved_model_banana/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
 freshly_saved_model_mango/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
 freshly_saved_model_orange/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+banana_saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+chili_saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+mango_saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+orange_saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+paprika_saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+tomato_saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

banana_saved_model/keras_metadata.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51dfb428765635b5fce639267bdade63078b16d1cadcfb559ebcbeda6177d512
+size 51029

banana_saved_model/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:257ef542157172cb183e728e7dd6e022c65fd4def4690674288ee241dc800ce4
+size 430850

banana_saved_model/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b0cc1ef25479f9ad3520addf6007fb834fe44664dc5475a7e94e496e58b2e17a
+size 2063728

chili_saved_model/keras_metadata.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:324f9d91cb1b2e8b3970a3522dcc48a11baa6c3ece2dc69865edf24a39d51685
+size 51026

chili_saved_model/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9aac7fc4f671562a22824f270e5851ccba0beadecd2177b447e7b3bb1db3673
+size 430399

chili_saved_model/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c792597c6fff5ade8694a297f5df493682e329c752494531cc9f5972363be780
+size 2063728

main.py

@@ -1,112 +1,98 @@
-import io
-import numpy as np
-import tensorflow as tf
-from fastapi import FastAPI, File, UploadFile, Form, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from PIL import Image
-
-app = FastAPI(
-    title="Freshly Universal API",
-    description="API deteksi kematangan buah dan sayur.",
-    version="1.0.1"
-)
-
-# --- 2. TAMBAHKAN BLOK KODE CORS INI ---
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"], # Mengizinkan website dari domain mana pun untuk mengakses API ini
-    allow_credentials=True,
-    allow_methods=["*"], # Mengizinkan semua metode (GET, POST, dll)
-    allow_headers=["*"],
-)
-
-# 1. Konfigurasi Model
-CONFIG = {
-    "banana":  ['banana_ripe', 'banana_rotten', 'banana_unripe'],
-    "mango":   ['mango_ripe', 'mango_rotten', 'mango_unripe'],
-    "orange":  ['orange_ripe', 'orange_rotten', 'orange_unripe'],
-    #"chili":   ['chili_ripe', 'chili_rotten', 'chili_unripe'],
-    #"paprika": ['paprika_ripe', 'paprika_rotten', 'paprika_unripe'],
-    #"tomato":  ['tomato_ripe', 'tomato_rotten', 'tomato_unripe']
-}
-
-IMG_SIZE = (224, 224)
-MEAN = np.array([0.485, 0.456, 0.406])
-STD = np.array([0.229, 0.224, 0.225])
-
-# 2. Memuat Semua Model ke Memori
-models = {}
-print("Memulai proses pemuatan semua model...")
-for fruit_type in CONFIG.keys():
-    model_folder = f"freshly_saved_model_{fruit_type}"
-    try:
-        models[fruit_type] = tf.keras.models.load_model(model_folder)
-        print(f" -> Model {fruit_type.upper()} berhasil dimuat.")
-    except Exception as e:
-        print(f" [X] Gagal memuat model {fruit_type}: {e}")
-
-# 3. Fungsi Preprocessing
-def preprocess_image(image_bytes):
-    try:
-        img = Image.open(io.BytesIO(image_bytes)).convert('RGB')
-        img = img.resize(IMG_SIZE)
-        img_array = tf.keras.utils.img_to_array(img)
-        img_array = img_array / 255.0
-        img_array = (img_array - MEAN) / STD
-        img_array = np.expand_dims(img_array, axis=0)
-        return img_array
-    except Exception as e:
-        raise ValueError(f"Gagal memproses gambar: {str(e)}")
-
-# ---------------------------------------------------------
-# ENDPOINT HEALTH CHECK (Untuk UptimeRobot)
-# ---------------------------------------------------------
-@app.get("/health")
-def health_check():
-    return {"status": "active", "message": "Server is awake and ready!"}
-
-# ---------------------------------------------------------
-# ENDPOINT PREDIKSI (Satu URL untuk semua model)
-# ---------------------------------------------------------
-@app.post("/predict")
-async def predict_fruit(
-    # Menerima teks (jenis buah) dan file (gambar) dalam satu Form yang sama
-    fruit_type: str = Form(..., description="Tulis: banana, mango, orange, chili, paprika, atau tomato"),
-    file: UploadFile = File(...)
-):
-    fruit_type = fruit_type.lower()
-    
-    # Validasi jenis buah
-    if fruit_type not in models:
-        raise HTTPException(
-            status_code=404, 
-            detail=f"Model '{fruit_type}' tidak ada. Pilihan: {list(models.keys())}"
-        )
-    
-    # Validasi file
-    if not file.content_type.startswith('image/'):
-        raise HTTPException(status_code=400, detail="File harus berupa gambar.")
-    
-    try:
-        contents = await file.read()
-        img_tensor = preprocess_image(contents)
-        
-        # Prediksi
-        active_model = models[fruit_type]
-        class_names = CONFIG[fruit_type]
-        
-        predictions = active_model.predict(img_tensor)
-        pred_index = np.argmax(predictions[0])
-        confidence = float(predictions[0][pred_index])
-        
-        return {
-            "fruit_type": fruit_type,
-            "filename": file.filename,
-            "predicted_class": class_names[pred_index],
-            "confidence": round(confidence * 100, 2),
-            "all_probabilities": {
-                class_names[i]: round(float(predictions[0][i]) * 100, 2) for i in range(len(class_names))
-            }
-        }
-    except Exception as e:
+import io
+import numpy as np
+import tensorflow as tf
+from fastapi import FastAPI, File, UploadFile, Form, HTTPException
+from PIL import Image
+
+app = FastAPI(
+    title="Freshly API",
+    description="API deteksi kematangan buah dan sayur.",
+    version="1.0.2"
+)
+
+# 1. Konfigurasi Model
+CONFIG = {
+    "banana":  ['banana_ripe', 'banana_rotten', 'banana_unripe'],
+    "mango":   ['mango_ripe', 'mango_rotten', 'mango_unripe'],
+    "orange":  ['orange_ripe', 'orange_rotten', 'orange_unripe'],
+    "chili":   ['chili_ripe', 'chili_rotten', 'chili_unripe'],
+    "paprika": ['paprika_ripe', 'paprika_rotten', 'paprika_unripe'],
+    "tomato":  ['tomato_ripe', 'tomato_rotten', 'tomato_unripe']
+}
+
+IMG_SIZE = (224, 224)
+MEAN = np.array([0.485, 0.456, 0.406])
+STD = np.array([0.229, 0.224, 0.225])
+
+# 2. Memuat Semua Model ke Memori
+models = {}
+print("Memulai proses pemuatan semua model...")
+for fruit_type in CONFIG.keys():
+    model_folder = f"{fruit_type}_saved_model"
+    try:
+        models[fruit_type] = tf.keras.models.load_model(model_folder)
+        print(f" -> Model {fruit_type.upper()} berhasil dimuat.")
+    except Exception as e:
+        print(f" [X] Gagal memuat model {fruit_type}: {e}")
+
+# 3. Fungsi Preprocessing
+def preprocess_image(image_bytes):
+    try:
+        img = Image.open(io.BytesIO(image_bytes)).convert('RGB')
+        img = img.resize(IMG_SIZE)
+        img_array = tf.keras.utils.img_to_array(img)
+        img_array = img_array / 255.0
+        img_array = (img_array - MEAN) / STD
+        img_array = np.expand_dims(img_array, axis=0)
+        return img_array
+    except Exception as e:
+        raise ValueError(f"Gagal memproses gambar: {str(e)}")
+
+# ENDPOINT HEALTH CHECK (Untuk UptimeRobot)
+@app.get("/health")
+def health_check():
+    return {"status": "active", "message": "Server is awake and ready!"}
+
+# ENDPOINT PREDIKSI (Satu URL untuk semua model)
+@app.post("/predict")
+async def predict_fruit(
+    # Menerima teks (jenis buah) dan file (gambar) dalam satu Form yang sama
+    fruit_type: str = Form(..., description="Tulis: banana, mango, orange, chili, paprika, atau tomato"),
+    file: UploadFile = File(...)
+):
+    fruit_type = fruit_type.lower()
+    
+    # Validasi jenis buah
+    if fruit_type not in models:
+        raise HTTPException(
+            status_code=404, 
+            detail=f"Model '{fruit_type}' tidak ada. Pilihan: {list(models.keys())}"
+        )
+    
+    # Validasi file
+    if not file.content_type.startswith('image/'):
+        raise HTTPException(status_code=400, detail="File harus berupa gambar.")
+    
+    try:
+        contents = await file.read()
+        img_tensor = preprocess_image(contents)
+        
+        # Prediksi
+        active_model = models[fruit_type]
+        class_names = CONFIG[fruit_type]
+        
+        predictions = active_model.predict(img_tensor)
+        pred_index = np.argmax(predictions[0])
+        confidence = float(predictions[0][pred_index])
+        
+        return {
+            "fruit_type": fruit_type,
+            "filename": file.filename,
+            "predicted_class": class_names[pred_index],
+            "confidence": round(confidence * 100, 2),
+            "all_probabilities": {
+                class_names[i]: round(float(predictions[0][i]) * 100, 2) for i in range(len(class_names))
+            }
+        }
+    except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))

mango_saved_model/keras_metadata.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a9e6e019c2a8bb4a6c22ac5bc9b34223748d0c8938a640d120920c4265936186
+size 51026

mango_saved_model/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7310f6fbfe95842d9d7f859229b4a45a5b79c1901f369a45d790ae2b6ac4943e
+size 429659

mango_saved_model/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:acb09087aa26cf4cc7f1c399742f4abcefd7a16aaea5bb74bec7ca9c22542c2e
+size 2063728

orange_saved_model/keras_metadata.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f12b8535b3ca9628e5f2b7a85aa16e07fdf2c15964785537d5b99b9ab0a33246
+size 51029

orange_saved_model/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b0b00a34ac5db224a428a5616765ca6c21624896f4992dab9a7b70d4513de59
+size 430110

orange_saved_model/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7b18a3914f82ca6b86adfefde7ed5e30f9761b0b086eadb9698014bd07ed85f
+size 2063728

paprika_saved_model/keras_metadata.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88834b962ec018d4dba61f3f2eb48bbf56254ee3c9296b27ce14379e4452521b
+size 51140

paprika_saved_model/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f89acc77fb077d0256f39e9dd3a008e549012c2b016cce1ce150a6cf2f7ecdfd
+size 432637

paprika_saved_model/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89686d99996c9c69dca60007451974d373ca028ef5d8432c3e933bf9da86b76c
+size 2063744

tomato_saved_model/keras_metadata.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7835216fc070946e00e9254596ac23cb784fa9bbe819669c206e2b90f0a5ba1d
+size 51029

tomato_saved_model/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7249074d9b98c5efc6d99b383111be64fc7bf87f0b18990321e735edd61b1cf3
+size 430110

tomato_saved_model/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:182f729158f0839b1944352e46e2c364d69c5cc50d4d6754999d213dc764a0f8
+size 2063728