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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
import requests
import os
# ==========================
# 1. Load your trained model
# ==========================
MODEL_PATH = "model1.h5"
if not os.path.exists(MODEL_PATH):
print("Downloading model from GitHub...")
url = "https://media.githubusercontent.com/media/Sedtha-019/face-recognition-model/main/model1.h5"
r = requests.get(url)
r.raise_for_status()
with open(MODEL_PATH, "wb") as f:
f.write(r.content)
print("✅ Model downloaded successfully!")
model = tf.keras.models.load_model(MODEL_PATH)
print("✅ Model loaded successfully!")
class_names = ["Sed", "Rak"]
def preprocess_image(image):
image = image.resize((300, 300))
img_array = np.array(image) / 255.0
img_array = np.expand_dims(img_array, axis=0)
return img_array
def predict(image):
if image is None:
return {"Error": 1.0}
img_array = preprocess_image(image)
prediction = model.predict(img_array)
return {class_names[i]: float(prediction[0][i]) for i in range(len(class_names))}
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Upload a Face Image"),
outputs=gr.Label(num_top_classes=2, label="Prediction"),
title="🧠 Face Recognition (Sedtha Model)",
description="Upload a face image — the model predicts who it is.",
theme="default"
)
# For Hugging Face, don't use share=True
iface.launch() |