Update app.py

app.py

@@ -3,126 +3,76 @@ import cv2
 import numpy as np
 from ultralytics import YOLO
 import easyocr
-import logging
-import torch.serialization  # Für Allowlist
-
-# Logging einrichten
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Allowlist für YOLOv8-Modell
-try:
-    logger.info("Adding ultralytics.nn.tasks.DetectionModel to PyTorch safe globals")
-    torch.serialization.add_safe_globals(['ultralytics.nn.tasks.DetectionModel'])
-except Exception as e:
-    logger.error("Failed to add safe globals: %s", str(e))
-    raise
 
 # 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
+model = YOLO("yolov8n.pt", weights_only=False)  # Nano-Modell, weights_only=False für einfaches Laden
 
 # 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
+reader = easyocr.Reader(['en'], gpu=False)  # Englisch, CPU für Free Tier
 
 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))
+    image_np = np.array(image)
+    image_cv = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
 
     # 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."}
+        return "Ein Candlestick-Chart mit grünen und roten Kerzen, Preisen auf der y-Achse und weißem Hintergrund."
 
     # 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."}
+        # 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()
+            for box in boxes:
+                # Filter für Kerzen (schmal und hoch)
+                xmin, ymin, xmax, ymax = map(int, box)
+                if (ymax - ymin) / (xmax - xmin) > 2:  # Verhältnis für Kerzen
+                    candle_roi = image_cv[ymin:ymax, xmin:xmax]
+                    if candle_roi.size == 0:
+                        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
+                    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 "Kein Preis erkannt"
+
+                    detections.append({
+                        "Kerze": f"Farbe: {color_rgb}, Preise: {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]
+        
+        if not detections:
+            return "Keine Kerzen erkannt. Bitte lade ein klares Bild mit sichtbaren Kerzen und Preisen hoch."
+
+        # Textantwort erstellen
+        response = "Letzte 8 Kerzen:\n"
+        for i, detection in enumerate(detections, 1):
+            response += f"Kerze {i}: {detection['Kerze']}\n"
+        return response
 
     else:
-        return {"prompt": prompt, "description": "Unsupported prompt. Use 'Was siehst du auf dem Bild?' or 'List last 8 candles with their colors'."}
+        return "Nicht unterstützter Prompt. Verwende 'Was siehst du auf dem Bild?' oder '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'")
+        gr.Image(type="pil", label="Bild hochladen"),
+        gr.Textbox(label="Prompt", placeholder="z. B. 'Was siehst du auf dem Bild?' oder '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."
+    outputs="text",
+    title="Einfache Chart-Analyse mit YOLOv8",
+    description="Lade einen TradingView-Screenshot hoch, um Kerzen zu analysieren oder eine Beschreibung zu erhalten."
 )
 
 iface.launch()