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app.py.py

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+import gradio as gr
+import cv2
+import numpy as np
+from datetime import datetime, timedelta
+from geometry import extract_candle_data, detect_valid_signal
+
+def predict_signal(image):
+    try:
+        # Resize for consistency
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        candle_data = extract_candle_data(image)
+        signal = detect_valid_signal(candle_data)
+        return signal if signal else "No signal (conditions not met)"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+iface = gr.Interface(
+    fn=predict_signal,
+    inputs=gr.Image(type="numpy", label="Upload Chart Screenshot"),
+    outputs=gr.Textbox(label="Signal Output"),
+    title="TRANSFINITY FINAL CORE v.ULTIMA",
+    description="Upload a Quotex OTC chart to get the next 1-minute binary options signal. Timezone: UTC+6"
+)
+
+if __name__ == "__main__":
+    iface.launch()

geometry.py

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+import numpy as np
+import cv2
+from datetime import datetime
+
+def extract_candle_data(image):
+    # Basic HSV-based detection placeholder
+    # You should replace with your detailed TRANSFINITY detection logic
+    return {
+        "candles": [],  # Populate with parsed candle info
+        "current_time": datetime.utcnow() + timedelta(hours=6),  # UTC+6
+        "asset": "UNKNOWN_OTC"
+    }
+
+def detect_valid_signal(data):
+    # Placeholder signal logic (replace with scoring engine)
+    current_time = data["current_time"]
+    time_str = current_time.strftime("%H:%M")
+    asset = data["asset"]
+
+    # Insert your advanced scoring + filter logic here
+    # For now, simulate a PUT signal
+    signal_ok = True  # Replace with your conditions
+
+    if signal_ok:
+        return f"{time_str} • {asset} • PUT"
+    return None

requirements.txt

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+gradio
+opencv-python
+numpy