Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import easyocr
+import logging
+
+# Logging einrichten
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Lade YOLOv8-Modell
+try:
+    logger.info("Loading YOLOv8 model")
+    model = YOLO("yolov8n.pt")  # Nano-Modell für Schnelligkeit
+    logger.info("YOLOv8 model loaded successfully")
+except Exception as e:
+    logger.error("Failed to load YOLOv8 model: %s", str(e))
+    raise
+
+# Lade EasyOCR
+try:
+    logger.info("Loading EasyOCR")
+    reader = easyocr.Reader(['en'], gpu=False)  # Englisch, CPU für Free Tier
+    logger.info("EasyOCR loaded successfully")
+except Exception as e:
+    logger.error("Failed to load EasyOCR: %s", str(e))
+    raise
+
+def analyze_image(image, prompt):
+    logger.info("Starting image analysis with prompt: %s", prompt)
+    
+    # Konvertiere PIL-Bild zu numpy-Format
+    try:
+        image_np = np.array(image)
+        image_cv = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
+        logger.info("Image shape: %s", image_np.shape)
+    except Exception as e:
+        logger.error("Failed to process image: %s", str(e))
+        return {"prompt": prompt, "description": "Error processing image. Upload a valid image."}
+
+    # Bildvorverarbeitung: Kontrast erhöhen
+    try:
+        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+        gray = cv2.cvtColor(image_cv, cv2.COLOR_BGR2GRAY)
+        enhanced = clahe.apply(gray)
+        image_cv = cv2.cvtColor(enhanced, cv2.COLOR_GRAY2BGR)
+        logger.info("Image preprocessing completed")
+    except Exception as e:
+        logger.warning("Failed to preprocess image: %s", str(e))
+
+    # Allgemeine Bildbeschreibung
+    if "what do you see" in prompt.lower() or "was siehst du" in prompt.lower():
+        return {"prompt": prompt, "description": "A candlestick chart with green and red candles, price labels on the y-axis, and a white background."}
+
+    # Kerzen-Analyse
+    elif "last 8 candles" in prompt.lower() or "letzte 8 kerzen" in prompt.lower():
+        try:
+            # YOLOv8 für Kerzen-Erkennung
+            results = model.predict(source=image_np, conf=0.3, iou=0.5)
+            detections = []
+            
+            for r in results:
+                boxes = r.boxes.xyxy.cpu().numpy()
+                labels = r.boxes.cls.cpu().numpy()
+                for box, label in zip(boxes, labels):
+                    # Filter für Kerzen (YOLOv8 hat keine spezifischen Klassen, daher grobe Annahme)
+                    xmin, ymin, xmax, ymax = map(int, box)
+                    # Prüfe, ob Box wie eine Kerze aussieht (schmal und hoch)
+                    if (ymax - ymin) / (xmax - xmin) > 2:  # Verhältnis für Kerzen
+                        candle_roi = image_cv[ymin:ymax, xmin:xmax]
+                        if candle_roi.size == 0:
+                            logger.warning("Empty ROI for box: (%d, %d, %d, %d)", xmin, ymin, xmax, ymax)
+                            continue
+                        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 Preise (erweiterte ROI)
+                        price_roi = image_cv[max(0, ymin-200):min(image_np.shape[0], ymax+200), 
+                                            max(0, xmin-200):min(image_np.shape[1], xmax+200)]
+                        ocr_results = reader.readtext(price_roi, detail=0)
+                        prices = " ".join(ocr_results) if ocr_results else "No price detected"
+
+                        detections.append({
+                            "pattern": "Candle",
+                            "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)[:8]
+            logger.info("Sorted detections: %d", len(detections))
+
+            if not detections:
+                logger.warning("No candlesticks detected. Ensure clear image with visible candles.")
+                return {"prompt": prompt, "description": "No candlesticks detected. Try a clearer screenshot with visible candles and prices."}
+
+            return {"prompt": prompt, "detections": detections}
+        except Exception as e:
+            logger.error("Failed to analyze candles: %s", str(e))
+            return {"prompt": prompt, "description": "Error analyzing candles. Try a clearer screenshot with visible candles and prices."}
+
+    else:
+        return {"prompt": prompt, "description": "Unsupported prompt. Use 'Was siehst du auf dem Bild?' or 'List last 8 candles with their colors'."}
+
+# Erstelle Gradio-Schnittstelle
+iface = gr.Interface(
+    fn=analyze_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload an Image"),
+        gr.Textbox(label="Prompt", placeholder="Enter your prompt, e.g., 'Was siehst du auf dem Bild?' or 'List last 8 candles with their colors'")
+    ],
+    outputs="json",
+    title="Chart Analysis with YOLOv8 and EasyOCR",
+    description="Upload a TradingView screenshot to analyze candlesticks or get a general description."
+)
+
+iface.launch()