import os
import json
import numpy as np
import tensorflow as tf
import gradio as gr
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.applications.efficientnet import preprocess_input

# ───────────────────────────────────────────────────────────────────────────────
# 1️⃣ CONFIGURATION
# ───────────────────────────────────────────────────────────────────────────────
MODEL_FILE          = "final_model.h5"
BEST_THRESHOLD_PATH = "best_threshold.json"
IMG_SIZE            = (32, 32)

# Load the threshold value
with open(BEST_THRESHOLD_PATH, "r") as f:
    best_threshold = json.load(f)["best_threshold"]

# Load the model
model = tf.keras.models.load_model(MODEL_FILE, compile=False)

# ───────────────────────────────────────────────────────────────────────────────
# 2️⃣ IMAGE PREPROCESSING AND PREDICTION FUNCTION
# ───────────────────────────────────────────────────────────────────────────────
def preprocess_image(image):
    # 1) Resize PIL image
    img = image.resize(IMG_SIZE)
    # 2) To array [0–255]
    arr = img_to_array(img)
    # 3) EfficientNet preprocessing → [-1,1]
    arr = preprocess_input(arr)
    # 4) Add batch axis → (1,32,32,3)
    return np.expand_dims(arr, axis=0)

def predict(image):
    x = preprocess_image(image)
    prob = model.predict(x, verbose=0).squeeze()
    if prob >= best_threshold:
        # FAKE
        percent = prob * 100
        label = f"❌ FAKE — {percent:.1f}% confidence"
        color = "red"
    else:
        # REAL
        percent = (1 - prob) * 100
        label = f"✅ REAL — {percent:.1f}% confidence"
        color = "green"
    return f"<div style='color: {color}; font-weight: bold;'>{label}</div>"

# ───────────────────────────────────────────────────────────────────────────────
# 3️⃣ GRADIO INTERFACE
# ───────────────────────────────────────────────────────────────────────────────
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.HTML(label="Prediction"),
    title="Is It Real? Find Out!",
    description="Upload any image and our AI model will tell you if it's real or fake.",
    live=False,               # set to False for one-shot prediction
    flagging_mode="never"
)

iface.launch()