Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+import cv2
+import numpy as np
+import easyocr
+
+# Lade das Modell
+model_path = hf_hub_download(repo_id="foduucom/stockmarket-pattern-detection-yolov8", filename="model.pt")
+model = YOLO(model_path)
+
+# OCR für Preise
+reader = easyocr.Reader(['en'])
+
+def analyze_image(image, prompt):
+    # Konvertiere PIL-Bild zu OpenCV-Format
+    image_np = np.array(image)
+    image_cv = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
+
+    # Führe Objekterkennung durch
+    results = model.predict(source=image_np, save=False)
+
+    # Extrahiere Kerzen
+    detections = []
+    for result in results:
+        for box in result.boxes:
+            label = result.names[int(box.cls)]
+            confidence = float(box.conf)
+            xmin, ymin, xmax, ymax = box.xyxy[0].tolist()
+
+            # Extrahiere Farbe
+            candle_roi = image_cv[int(ymin):int(ymax), int(xmin):int(xmax)]
+            mean_color = np.mean(candle_roi, axis=(0, 1)).astype(int)
+            color_rgb = f"RGB({mean_color[2]},{mean_color[1]},{mean_color[0]})"
+
+            # OCR für Opening/Close-Preise (aus Achsen, anpassen an Chart)
+            price_text = reader.readtext(image_cv[int(ymin):int(ymax), int(xmin):int(xmax)], detail=0)
+            prices = ' '.join(price_text) if price_text else "No price detected"
+
+            detections.append({
+                "pattern": label,
+                "confidence": confidence,
+                "color": color_rgb,
+                "prices": prices,
+                "x_center": (xmin + xmax) / 2
+            })
+
+    # Sortiere nach x-Position (rechts nach links = neueste Kerzen)
+    detections = sorted(detections, key=lambda x: x["x_center"], reverse=True)
+
+    # Begrenze auf die letzten 8 Kerzen
+    if "last 8 candles" in prompt.lower() or "letzte 8 kerzen" in prompt.lower():
+        detections = detections[:8]
+
+    return detections
+
+iface = gr.Interface(
+    fn=analyze_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload TradingView Screenshot"),
+        gr.Textbox(label="Prompt", placeholder="Enter your prompt, e.g., 'List last 8 candles with their colors'")
+    ],
+    outputs="json",
+    title="Stock Chart Analysis",
+    description="Upload a screenshot and provide a prompt to analyze candlesticks."
+)
+
+iface.launch()