Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+import mediapipe as mp
+import torch
+import torchaudio
+from scipy.signal import chirp
+import plotly.graph_objects as go
+
+# Load MediaPipe Hand Tracking
+mp_hands = mp.solutions.hands
+mp_drawing = mp.solutions.drawing_utils
+hands = mp_hands.Hands(min_detection_confidence=0.7, min_tracking_confidence=0.7)
+
+# Generate Quantum Wave Sound
+def generate_wave(frequency=220, duration=3):
+    sample_rate = 44100
+    t = np.linspace(0, duration, int(sample_rate * duration))
+    waveform = chirp(t, f0=frequency, f1=frequency*2, t1=duration, method='quadratic')
+    return waveform, sample_rate
+
+# 3D Black Hole with Hand Interaction
+def process_frame(frame):
+    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    results = hands.process(frame_rgb)
+
+    if results.multi_hand_landmarks:
+        for hand_landmarks in results.multi_hand_landmarks:
+            mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
+            x = hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].x
+            y = hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].y
+            rotation = (x - 0.5) * 360  # Rotate black hole based on hand position
+            return frame, rotation
+
+    return frame, 0  # Default if no hand detected
+
+def black_hole_visualization(rotation):
+    fig = go.Figure(go.Surface(z=np.sin(np.linspace(0, 10, 100)[:, None] + np.linspace(0, 10, 100))))
+    fig.update_layout(scene_camera=dict(eye=dict(x=1.5*np.cos(np.radians(rotation)), y=1.5*np.sin(np.radians(rotation)), z=1)))
+    return fig
+
+def run_app(webcam):
+    frame, rotation = process_frame(webcam)
+    black_hole_figure = black_hole_visualization(rotation)
+    wave_data, sr = generate_wave(frequency=200 + rotation)
+    return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), black_hole_figure, (wave_data, sr)
+
+iface = gr.Interface(fn=run_app, 
+                     inputs=gr.Image(source="webcam", streaming=True), 
+                     outputs=["image", "plot", "audio"], 
+                     live=True)
+
+iface.launch()