webcam

app.py

@@ -1,60 +1,49 @@
 import gradio as gr
-import requests
-import os
-
-# Функция диагностики
-def diagnose():
-    token = os.environ.get("HF_TOKEN", "")
-    lines = ["🔍 Диагностика HF API", "="*40]
-
-    # Проверка токена
-    lines.append(f"Токен: {'✅ Есть' if token else '❌ Нет'}")
-
-    # Проверка моделей
-    models = ["gpt2", "microsoft/phi-2"]
-    for model in models:
-        try:
-            headers = {"Authorization": f"Bearer {token}"} if token else {}
-            response = requests.post(
-                f"https://api-inference.huggingface.co/models/{model}",
-                headers=headers,
-                json={"inputs": "test", "parameters": {"max_length": 1}},
-                timeout=10
-            )
-            status = f"{model}: {'✅' if response.status_code == 200 else '❌ ' + str(response.status_code)}"
-        except:
-            status = f"{model}: ❌ Ошибка"
-        lines.append(status)
-
-    return "\n".join(lines)
-
-# Функция генерации
-def generate(prompt):
-    try:
-        response = requests.post(
-            "https://api-inference.huggingface.co/models/gpt2",
-            json={"inputs": prompt, "parameters": {"max_length": 100}},
-            timeout=30
-        )
-        if response.status_code == 200:
-            return response.json()[0]['generated_text']
-        return f"Ошибка {response.status_code}"
-    except:
-        return "Ошибка соединения"
-
-# Интерфейс
-with gr.Blocks() as demo:
-    gr.Markdown("# ИИ для игры")
-
-    with gr.Tab("🤖 Генерация"):
-        prompt = gr.Textbox(label="Промпт")
-        generate_btn = gr.Button("Сгенерировать")
-        output = gr.Textbox(label="Ответ")
-        generate_btn.click(generate, prompt, output)
-
-    with gr.Tab("🩺 Диагностика"):
-        diagnose_btn = gr.Button("Запустить диагностику")
-        diagnose_output = gr.Textbox(label="Результат", lines=10)
-        diagnose_btn.click(diagnose, outputs=diagnose_output)
+import cv2
+import numpy as np
+
+# Load face detector (OpenCV's built-in classifier)
+face_cascade = cv2.CascadeClassifier(
+    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
+)
+
+def process_frame(frame):
+    """
+    Process each webcam frame: detect faces and overlay masks.
+    """
+    if frame is None:
+        return None
+
+    # Convert to grayscale for face detection
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+    # Apply mask to each detected face
+    for (x, y, w, h) in faces:
+        # Zorro-style mask (black band across eyes)
+        mask_y = y + int(h * 0.35)
+        mask_height = int(h * 0.3)
+        cv2.rectangle(frame, (x, mask_y), (x + w, mask_y + mask_height), (0, 0, 0), -1)
+
+        # Eye cutouts for the mask (so you can see!)
+        eye_y = mask_y + int(mask_height * 0.5)
+        cv2.circle(frame, (x + int(w * 0.3), eye_y), int(w * 0.12), (255, 255, 255), -1)
+        cv2.circle(frame, (x + int(w * 0.7), eye_y), int(w * 0.12), (255, 255, 255), -1)
+
+        # Optional: Add a "Z" mark
+        cv2.putText(frame, "Z", (x + w - 20, y + h - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
+
+    return frame
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=process_frame,
+    inputs=gr.Image(sources=["webcam"], streaming=True),
+    outputs=gr.Image(type="numpy"),
+    live=True,
+    title="🎭 Zorro Mask Sandbox",
+    description="Point your webcam at your face. The app detects faces and adds a Zorro-style mask in real-time!"
+)
 
 demo.launch()

requirements.txt

@@ -1,4 +1,3 @@
-gradio>=4.0
-requests>=2.28
-huggingface-hub
-transformers
+gradio==4.19.2
+opencv-python==4.9.0.80
+numpy==1.24.3