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import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor
import torch
import numpy as np
import cv2
import logging
# Logging einrichten
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Lade das Modell und den Processor
try:
logger.info("Loading model: microsoft/florence-2-base")
model = AutoModelForCausalLM.from_pretrained("microsoft/florence-2-base", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("microsoft/florence-2-base", trust_remote_code=True)
logger.info("Model and processor loaded successfully")
except Exception as e:
logger.error("Failed to load model: %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. Ensure a valid image is uploaded."}
# 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():
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():
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]})"
# 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
})
# 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="Image Analysis with Florence-2-base",
description="Upload an image to analyze candlesticks or get a general description."
)
iface.launch() |