hoooollll

app.py

@@ -1,63 +1,37 @@
 import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-
 import numpy as np
 from PIL import Image
 import tensorflow as tf
 from tensorflow.keras.models import load_model
 from tensorflow.keras import layers, Model
 import joblib
+import gradio as gr
 import cv2
-import h5py
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
-from contextlib import asynccontextmanager
-
+from custom_objects import get_custom_objects
 # ======================================================
 # CONFIG
 # ======================================================
 IMG_SIZE = 224
 
 # ======================================================
-# CUSTOM LAYERS
+# CUSTOM LAYERS (for H5 loading)
 # ======================================================
 class SimpleMultiHeadAttention(layers.Layer):
     def __init__(self, num_heads=8, key_dim=64, **kwargs):
         super().__init__(**kwargs)
         self.mha = layers.MultiHeadAttention(num_heads=num_heads, key_dim=key_dim)
-
+        
     def call(self, x):
         return self.mha(x, x)
 
-def get_custom_objects():
-    return {
-        'SimpleMultiHeadAttention': SimpleMultiHeadAttention,
-        'MultiHeadAttention': layers.MultiHeadAttention,
-        'Dropout': layers.Dropout
-    }
+#def get_custom_objects():
+   # return {
+    #    'FixedDropout': layers.Dropout,
+    #    'MultiHeadAttention': layers.MultiHeadAttention,
+   # }
 
 # ======================================================
-# FIX MISSING 'predictions' GROUP IN H5 FILE
-# ======================================================
-def fix_missing_predictions(h5_path):
-    try:
-        with h5py.File(h5_path, "r+") as f:
-            if "model_weights" not in f:
-                print("⚠️ H5 file has no 'model_weights' group — cannot fix this model.")
-                return
-            pred_path = "model_weights/predictions"
-            if pred_path in f:
-                return
-            grp = f.require_group(pred_path)
-            if "weight_names" not in grp.attrs:
-                grp.attrs.create("weight_names", [])
-    except Exception as e:
-        print("❌ Failed to edit H5:", e)
-
-# ======================================================
-# FALLBACK FEATURE EXTRACTOR
+# FEATURE EXTRACTOR CREATION (fallback)
 # ======================================================
 def create_fallback_extractor():
     base_model = tf.keras.applications.MobileNetV2(
@@ -67,6 +41,7 @@ def create_fallback_extractor():
         pooling='avg'
     )
     base_model.trainable = False
+    
     inputs = tf.keras.Input(shape=(IMG_SIZE, IMG_SIZE, 3))
     x = tf.keras.applications.mobilenet_v2.preprocess_input(inputs)
     features = base_model(x, training=False)
@@ -74,97 +49,99 @@ def create_fallback_extractor():
     x = layers.Dropout(0.3)(x)
     x = layers.Dense(256, activation='relu')(x)
     outputs = layers.Dense(512, activation='relu')(x)
-    return Model(inputs, outputs)
+    
+    model = Model(inputs, outputs)
+    return model
 
 # ======================================================
 # LOAD MODELS
 # ======================================================
-extractor, classifier = None, None
-
 def load_models():
     global extractor, classifier
-    # Load feature extractor
+    
+    # Load extractor
     try:
-        fix_missing_predictions("hybrid_model.keras")
-        extractor = load_model("hybrid_model.keras", custom_objects=get_custom_objects(), compile=False)
-        print("✔ Feature extractor loaded")
+        print("Loading feature extractor (moodeli.h5)...")
+        with h5py.File("moodeli.h5", "r") as f:
+        print(f["model_weights"].keys())
+
+        
+        
+        extractor = load_model("moodeli.h5", custom_objects=get_custom_objects(), compile=False)
+        print("✓ Feature extractor loaded successfully")
     except Exception as e:
-        print(f"⚠ Failed to load extractor: {e}")
+        print(f"✗ Failed to load H5 extractor: {str(e)[:200]}")
+        print("Creating fallback extractor...")
         extractor = create_fallback_extractor()
-        print("✔ Fallback extractor created")
+        print("✓ Fallback extractor created")
+
     # Load classifier
     try:
+        print("Loading classifier (gbdt_model.pkl)...")
         classifier = joblib.load("gbdt_model.pkl")
-        print("✔ Classifier loaded")
+        print(f"✓ Classifier loaded ({type(classifier).__name__})")
     except Exception as e:
-        print(f"⚠ Failed to load classifier: {e}")
+        print(f"✗ Failed to load classifier: {str(e)[:200]}")
         from sklearn.ensemble import AdaBoostClassifier
         from sklearn.tree import DecisionTreeClassifier
         classifier = AdaBoostClassifier(
             estimator=DecisionTreeClassifier(max_depth=3),
             n_estimators=50,
-            random_state=40
+            random_state=42
         )
         dummy_features = np.random.randn(10, extractor.output_shape[-1])
         dummy_labels = np.random.randint(0, 2, 10)
         classifier.fit(dummy_features, dummy_labels)
-        print("✔ Dummy classifier created")
+        joblib.dump(classifier, "classifier.pkl")
+        print("✓ Dummy classifier created and saved")
 
 # ======================================================
 # IMAGE PREPROCESSING
 # ======================================================
-def preprocess_image(img: Image.Image):
-    img = np.array(img)
+def preprocess_image(img):
+    if isinstance(img, Image.Image):
+        img = np.array(img)
+    if len(img.shape) == 3 and img.shape[2] == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
     img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
     img = img.astype("float32") / 255.0
-    if len(img.shape) == 2:
-        img = np.stack([img]*3, axis=-1)
     return np.expand_dims(img, axis=0)
 
 # ======================================================
-# PREDICTION
+# PREDICTION FUNCTION
 # ======================================================
-def predict_image(img: Image.Image):
-    img_pre = preprocess_image(img)
-    features = extractor.predict(img_pre, verbose=0).flatten().reshape(1, -1)
+def predict(image):
+    if image is None:
+        return [("No Image", 0.0)]
+    
+    img_pre = preprocess_image(image)
+    features = extractor.predict(img_pre, verbose=0).flatten()
+    features = features.reshape(1, -1)
+    
     pred = classifier.predict(features)[0]
     try:
         proba = classifier.predict_proba(features)[0]
         confidence = proba[pred] * 100
     except:
-        confidence = 85.0
+        confidence = 85.0  # default
+    
     label = "Real" if pred == 0 else "Fake"
-    return {"label": label, "confidence": float(confidence)}
+    return {label: confidence}
 
 # ======================================================
-# LIFESPAN + FASTAPI APP
+# MAIN (Hugging Face Spaces)
 # ======================================================
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    print("⚡ Starting app and loading models...")
+if __name__ == "__main__":
+    print("Loading models...")
     load_models()
-    yield
-    print("⚡ Shutting down app...")
-
-app = FastAPI(title="Fake Image Detector API", lifespan=lifespan)
-
-# CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"]
-)
-
-# ROUTES
-@app.get("/")
-def root():
-    return {"message": "API is running!"}
-
-@app.post("/predict/")
-async def predict_endpoint(file: UploadFile = File(...)):
-    try:
-        img = Image.open(file.file).convert("RGB")
-        return JSONResponse(predict_image(img))
-    except Exception as e:
-        return JSONResponse({"error": str(e)}, status_code=400)
+    print("Models loaded successfully!")
+
+    iface = gr.Interface(
+        fn=predict,
+        inputs=gr.Image(type="pil", label="📷 Upload Image"),
+        outputs=gr.Label(num_top_classes=2, label="🎯 Prediction"),
+        title="🔍 Fake Image Detector",
+        description="Upload an image to detect if it's Real or Fake."
+    )
+    
+    iface.launch()

app_o.py

@@ -0,0 +1,170 @@
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras import layers, Model
+import joblib
+import cv2
+import h5py
+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from contextlib import asynccontextmanager
+
+# ======================================================
+# CONFIG
+# ======================================================
+IMG_SIZE = 224
+
+# ======================================================
+# CUSTOM LAYERS
+# ======================================================
+class SimpleMultiHeadAttention(layers.Layer):
+    def __init__(self, num_heads=8, key_dim=64, **kwargs):
+        super().__init__(**kwargs)
+        self.mha = layers.MultiHeadAttention(num_heads=num_heads, key_dim=key_dim)
+
+    def call(self, x):
+        return self.mha(x, x)
+
+def get_custom_objects():
+    return {
+        'SimpleMultiHeadAttention': SimpleMultiHeadAttention,
+        'MultiHeadAttention': layers.MultiHeadAttention,
+        'Dropout': layers.Dropout
+    }
+
+# ======================================================
+# FIX MISSING 'predictions' GROUP IN H5 FILE
+# ======================================================
+def fix_missing_predictions(h5_path):
+    try:
+        with h5py.File(h5_path, "r+") as f:
+            if "model_weights" not in f:
+                print("⚠️ H5 file has no 'model_weights' group — cannot fix this model.")
+                return
+            pred_path = "model_weights/predictions"
+            if pred_path in f:
+                return
+            grp = f.require_group(pred_path)
+            if "weight_names" not in grp.attrs:
+                grp.attrs.create("weight_names", [])
+    except Exception as e:
+        print("❌ Failed to edit H5:", e)
+
+# ======================================================
+# FALLBACK FEATURE EXTRACTOR
+# ======================================================
+def create_fallback_extractor():
+    base_model = tf.keras.applications.MobileNetV2(
+        input_shape=(IMG_SIZE, IMG_SIZE, 3),
+        include_top=False,
+        weights='imagenet',
+        pooling='avg'
+    )
+    base_model.trainable = False
+    inputs = tf.keras.Input(shape=(IMG_SIZE, IMG_SIZE, 3))
+    x = tf.keras.applications.mobilenet_v2.preprocess_input(inputs)
+    features = base_model(x, training=False)
+    x = layers.Dense(512, activation='relu')(features)
+    x = layers.Dropout(0.3)(x)
+    x = layers.Dense(256, activation='relu')(x)
+    outputs = layers.Dense(512, activation='relu')(x)
+    return Model(inputs, outputs)
+
+# ======================================================
+# LOAD MODELS
+# ======================================================
+extractor, classifier = None, None
+
+def load_models():
+    global extractor, classifier
+    # Load feature extractor
+    try:
+        fix_missing_predictions("hybrid_model.keras")
+        extractor = load_model("hybrid_model.keras", custom_objects=get_custom_objects(), compile=False)
+        print("✔ Feature extractor loaded")
+    except Exception as e:
+        print(f"⚠ Failed to load extractor: {e}")
+        extractor = create_fallback_extractor()
+        print("✔ Fallback extractor created")
+    # Load classifier
+    try:
+        classifier = joblib.load("gbdt_model.pkl")
+        print("✔ Classifier loaded")
+    except Exception as e:
+        print(f"⚠ Failed to load classifier: {e}")
+        from sklearn.ensemble import AdaBoostClassifier
+        from sklearn.tree import DecisionTreeClassifier
+        classifier = AdaBoostClassifier(
+            estimator=DecisionTreeClassifier(max_depth=3),
+            n_estimators=50,
+            random_state=40
+        )
+        dummy_features = np.random.randn(10, extractor.output_shape[-1])
+        dummy_labels = np.random.randint(0, 2, 10)
+        classifier.fit(dummy_features, dummy_labels)
+        print("✔ Dummy classifier created")
+
+# ======================================================
+# IMAGE PREPROCESSING
+# ======================================================
+def preprocess_image(img: Image.Image):
+    img = np.array(img)
+    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+    img = img.astype("float32") / 255.0
+    if len(img.shape) == 2:
+        img = np.stack([img]*3, axis=-1)
+    return np.expand_dims(img, axis=0)
+
+# ======================================================
+# PREDICTION
+# ======================================================
+def predict_image(img: Image.Image):
+    img_pre = preprocess_image(img)
+    features = extractor.predict(img_pre, verbose=0).flatten().reshape(1, -1)
+    pred = classifier.predict(features)[0]
+    try:
+        proba = classifier.predict_proba(features)[0]
+        confidence = proba[pred] * 100
+    except:
+        confidence = 85.0
+    label = "Real" if pred == 0 else "Fake"
+    return {"label": label, "confidence": float(confidence)}
+
+# ======================================================
+# LIFESPAN + FASTAPI APP
+# ======================================================
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    print("⚡ Starting app and loading models...")
+    load_models()
+    yield
+    print("⚡ Shutting down app...")
+
+app = FastAPI(title="Fake Image Detector API", lifespan=lifespan)
+
+# CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"]
+)
+
+# ROUTES
+@app.get("/")
+def root():
+    return {"message": "API is running!"}
+
+@app.post("/predict/")
+async def predict_endpoint(file: UploadFile = File(...)):
+    try:
+        img = Image.open(file.file).convert("RGB")
+        return JSONResponse(predict_image(img))
+    except Exception as e:
+        return JSONResponse({"error": str(e)}, status_code=400)