Update app.py

app.py

@@ -23,87 +23,103 @@ def analyze_image(image, prompt):
     logger.info("Starting image analysis with prompt: %s", prompt)
     
     # Konvertiere PIL-Bild zu numpy-Format
-    image_np = np.array(image)
-    image_cv = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
-    logger.info("Image shape: %s", image_np.shape)
+    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. Ensure a valid image is uploaded."}
 
     # 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)
+    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():
-        inputs = processor(text=prompt, images=image_np, return_tensors="pt")
-        with torch.no_grad():
-            outputs = model.generate(
-                input_ids=inputs["input_ids"],
-                pixel_values=inputs["pixel_values"],
-                max_length=1024,
-                num_beams=3
-            )
-        description = processor.batch_decode(outputs, skip_special_tokens=True)[0]
-        return {"prompt": prompt, "description": description}
+        try:
+            inputs = processor(text=prompt, images=image_np, return_tensors="pt")
+            with torch.no_grad():
+                outputs = model.generate(
+                    input_ids=inputs["input_ids"],
+                    pixel_values=inputs["pixel_values"],
+                    max_length=1024,
+                    num_beams=3
+                )
+            description = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+            return {"prompt": prompt, "description": description}
+        except Exception as e:
+            logger.error("Failed to generate description: %s", str(e))
+            return {"prompt": prompt, "description": "Error generating description. Try again with a clear image."}
 
     # Kerzen-Analyse
     elif "last 8 candles" in prompt.lower() or "letzte 8 kerzen" in prompt.lower():
-        task_prompt = "<OD>"  # Objekterkennung
-        inputs = processor(text=task_prompt, images=image_np, return_tensors="pt")
-        with torch.no_grad():
-            outputs = model.generate(
-                input_ids=inputs["input_ids"],
-                pixel_values=inputs["pixel_values"],
-                max_length=1024,
-                num_beams=3
-            )
-        predictions = processor.post_process_generation(outputs, task=task_prompt, image_size=(image_np.shape[1], image_np.shape[0]))
-        logger.info("Detected objects: %s", predictions)
+        try:
+            task_prompt = "<OD>"  # Objekterkennung
+            inputs = processor(text=task_prompt, images=image_np, return_tensors="pt")
+            with torch.no_grad():
+                outputs = model.generate(
+                    input_ids=inputs["input_ids"],
+                    pixel_values=inputs["pixel_values"],
+                    max_length=1024,
+                    num_beams=3
+                )
+            predictions = processor.post_process_generation(outputs, task=task_prompt, image_size=(image_np.shape[1], image_np.shape[0]))
+            logger.info("Detected objects: %s", predictions)
 
-        detections = []
-        if "<OD>" in predictions:
-            for i, (bbox, label) in enumerate(zip(predictions["<OD>"]["bboxes"], predictions["<OD>"]["labels"])):
-                # Erweitere Filter für Kerzen
-                if "candle" not in label.lower() and "bar" not in label.lower() and "chart" not in label.lower() and "candlestick" not in label.lower():
-                    continue
-                xmin, ymin, xmax, ymax = map(int, bbox)
-                
-                # Extrahiere Farbe
-                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]})"
+            detections = []
+            if "<OD>" in predictions:
+                for i, (bbox, label) in enumerate(zip(predictions["<OD>"]["bboxes"], predictions["<OD>"]["labels"])):
+                    # Erweitere Filter für Kerzen
+                    if "candle" not in label.lower() and "bar" not in label.lower() and "chart" not in label.lower() and "candlestick" not in label.lower():
+                        continue
+                    xmin, ymin, xmax, ymax = map(int, bbox)
+                    
+                    # Extrahiere Farbe
+                    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_inputs = processor(text="<OCR>", images=price_roi, return_tensors="pt")
-                with torch.no_grad():
-                    ocr_outputs = model.generate(
-                        input_ids=ocr_inputs["input_ids"],
-                        pixel_values=ocr_inputs["pixel_values"],
-                        max_length=1024
-                    )
-                prices = processor.batch_decode(ocr_outputs, skip_special_tokens=True)[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_inputs = processor(text="<OCR>", images=price_roi, return_tensors="pt")
+                    with torch.no_grad():
+                        ocr_outputs = model.generate(
+                            input_ids=ocr_inputs["input_ids"],
+                            pixel_values=ocr_inputs["pixel_values"],
+                            max_length=1024
+                        )
+                    prices = processor.batch_decode(ocr_outputs, skip_special_tokens=True)[0]
 
-                detections.append({
-                    "pattern": label,
-                    "color": color_rgb,
-                    "prices": prices if prices else "No price detected",
-                    "x_center": (xmin + xmax) / 2
-                })
+                    detections.append({
+                        "pattern": label,
+                        "color": color_rgb,
+                        "prices": prices if prices else "No price detected",
+                        "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))
+            # 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."}
+            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}
+            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'."}