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