Ahmedhany1 commited on
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Create app.py

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  1. app.py +63 -0
app.py ADDED
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+ import gradio as gr
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+ import cv2
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+ import torch
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+ import numpy as np
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+ from facenet_pytorch import MTCNN
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+ from transformers import pipeline
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+
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+ # 1) Initialize device, face detector, and HF pipelines
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+ mtcnn = MTCNN(keep_all=True, device=device)
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+
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+ age_pipe = pipeline(
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+ "image-classification",
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+ model="nateraw/vit-age-classifier",
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+ device=0 if device.type=="cuda" else -1
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+ )
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+ gender_pipe = pipeline(
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+ "image-classification",
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+ model="prithivMLmods/Gender-Classifier-Mini",
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+ device=0 if device.type=="cuda" else -1
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+ )
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+
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+ # 2) Frame annotation function
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+ def annotate(frame: np.ndarray) -> np.ndarray:
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+ """
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+ Input: H×W×3 RGB frame as NumPy array
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+ Output: same frame with boxes + age/gender labels drawn
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+ """
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+ img = frame.copy()
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+ boxes, _ = mtcnn.detect(img)
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+ if boxes is not None:
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+ for box in boxes:
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+ x1, y1, x2, y2 = map(int, box)
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+ face = img[y1:y2, x1:x2]
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+ # HF pipelines accept NumPy RGB directly
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+ age_pred = age_pipe(face)[0]
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+ gender_pred = gender_pipe(face)[0]
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+ label = (
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+ f"{gender_pred['label']} {gender_pred['score']:.2f}, "
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+ f"{age_pred['label']} {age_pred['score']:.2f}"
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+ )
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+ cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
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+ cv2.putText(
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+ img, label, (x1, y1 - 10),
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+ cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2
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+ )
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+ return img
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+
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+ # 3) Build Gradio interface
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+ iface = gr.Interface(
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+ fn=annotate,
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+ inputs=gr.Image(source="webcam", tool="editor", streaming=True),
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+ outputs=gr.Image(type="numpy"),
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+ title="Real-Time Age & Gender Prediction",
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+ description=(
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+ "Allow webcam access in your browser; faces will be boxed with age & gender labels."
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+ ),
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+ live=True
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+ )
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+
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+ # 4) Launch for HF Spaces (no share=True needed)
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+ if __name__ == "__main__":
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+ iface.launch(server_name="0.0.0.0", server_port=7860)