f

.gitignore

@@ -0,0 +1,22 @@
+cat <<EOF > .gitignore
+# Models
+*.keras
+*.h5
+*.pkl
+
+# Training scripts
+convert.py
+custom_objects.py
+deletedmodel.py
+load_and_test_model.py
+repair_model.py
+preprocessing.py
+app_old.py
+
+# Docker
+Dockerfile
+
+# Python cache
+__pycache__/
+*.pyc
+EOF

app.py

@@ -0,0 +1,160 @@
+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
+
+# ======================================================
+# CONFIG
+# ======================================================
+IMG_SIZE = 224
+
+# ======================================================
+# 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
+    }
+
+# ======================================================
+# 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)
+    model = Model(inputs, outputs)
+    return model
+
+# ======================================================
+# LOAD MODELS
+# ======================================================
+extractor, classifier = None, None
+
+def load_models():
+    global extractor, classifier
+    try:
+        fix_missing_predictions("hybrid_model.keras")
+        extractor = load_model("hybrid_model.keras", custom_objects=get_custom_objects(), compile=False)
+    except Exception as e:
+        extractor = create_fallback_extractor()
+
+    try:
+        classifier = joblib.load("gbdt_model.pkl")
+    except Exception as 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)
+
+# ======================================================
+# 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)}
+
+# ======================================================
+# FASTAPI APP
+# ======================================================
+app = FastAPI(title="Fake Image Detector API")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"]
+)
+
+@app.on_event("startup")
+def startup_event():
+    load_models()
+
+@app.get("/")
+def root():
+    return {"message": "Fake Image Detector API is running!"}
+
+@app.post("/predict/")
+async def predict_endpoint(file: UploadFile = File(...)):
+    try:
+        img = Image.open(file.file).convert("RGB")
+        result = predict_image(img)
+        return JSONResponse(result)
+    except Exception as e:
+        return JSONResponse({"error": str(e)}, status_code=400)
+

requirements.txt

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+fastapi
+uvicorn[standard]
+# Core frameworks
+tensorflow==2.10.0
+keras==2.10.0
+tensorflow-addons==0.20.0
+protobuf==3.19.6
+
+# ViT
+vit-keras==0.1.2
+
+# Image processing
+numpy==1.24.3
+opencv-python-headless==4.8.1.78
+pillow==10.1.0
+h5py==3.10.0
+
+# Machine learning / pipeline
+scikit-learn==1.3.2
+joblib
+
+# Web API
+fastapi==0.110.0
+uvicorn==0.24.0
+gradio==4.44.1
+pydantic==2.10.4
+streamlit