Update app.py

app.py

@@ -4,21 +4,36 @@ from ultralytics import YOLO
 import cv2
 import numpy as np
 import easyocr
+import logging
+
+# Logging einrichten
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 # 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'])
+reader = easyocr.Reader(['en'], gpu=False)
 
 def analyze_image(image, prompt):
+    logger.info("Starting image analysis with prompt: %s", prompt)
+    
     # Konvertiere PIL-Bild zu OpenCV-Format
     image_np = np.array(image)
     image_cv = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
+    
+    # Bildvorverarbeitung: Kontrast erhöhen
+    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 preprocessed: shape=%s", image_np.shape)
 
     # Führe Objekterkennung durch
-    results = model.predict(source=image_np, save=False)
+    results = model.predict(source=image_np, conf=0.3, iou=0.5, save=False)
+    logger.info("YOLO predictions: %d boxes detected", len(results[0].boxes))
 
     # Extrahiere Kerzen
     detections = []
@@ -27,15 +42,23 @@ def analyze_image(image, prompt):
             label = result.names[int(box.cls)]
             confidence = float(box.conf)
             xmin, ymin, xmax, ymax = box.xyxy[0].tolist()
+            logger.info("Detected: %s, confidence=%.2f, box=(%.0f, %.0f, %.0f, %.0f)", 
+                       label, confidence, xmin, ymin, xmax, ymax)
 
-            # Extrahiere Farbe
+            # Extrahiere Farbe (Fokus auf Kerzenkörper)
             candle_roi = image_cv[int(ymin):int(ymax), int(xmin):int(xmax)]
+            if candle_roi.size == 0:
+                logger.warning("Empty ROI for box: (%.0f, %.0f, %.0f, %.0f)", 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 Opening/Close-Preise (aus Achsen, anpassen an Chart)
-            price_text = reader.readtext(image_cv[int(ymin):int(ymax), int(xmin):int(xmax)], detail=0)
+            # OCR für Preise (erweitere ROI für Achsen)
+            price_roi = image_cv[max(0, int(ymin)-50):min(image_np.shape[0], int(ymax)+50), 
+                                max(0, int(xmin)-50):min(image_np.shape[1], int(xmax)+50)]
+            price_text = reader.readtext(price_roi, detail=0, allowlist='0123456789.')
             prices = ' '.join(price_text) if price_text else "No price detected"
+            logger.info("OCR prices: %s", prices)
 
             detections.append({
                 "pattern": label,
@@ -47,13 +70,23 @@ def analyze_image(image, prompt):
 
     # Sortiere nach x-Position (rechts nach links = neueste Kerzen)
     detections = sorted(detections, key=lambda x: x["x_center"], reverse=True)
+    logger.info("Sorted detections: %d", len(detections))
 
     # 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
+    # Debugging: Wenn leer, gib Hinweis
+    if not detections:
+        logger.warning("No detections found. Check image quality or model configuration.")
+        return {"prompt": prompt, "description": "No candlesticks detected. Ensure clear image and visible candles."}
+    
+    return {
+        "prompt": prompt,
+        "detections": detections
+    }
 
+# Erstelle Gradio-Schnittstelle
 iface = gr.Interface(
     fn=analyze_image,
     inputs=[
@@ -61,8 +94,8 @@ iface = gr.Interface(
         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."
+    title="Stock Chart Analysis with YOLOv8",
+    description="Upload a TradingView screenshot to detect the last 8 candlesticks, their colors, and prices."
 )
 
 iface.launch()