Spaces:
Running
Running
Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import gradio as gr
|
2 |
+
from huggingface_hub import hf_hub_download
|
3 |
+
from ultralytics import YOLO
|
4 |
+
import torch
|
5 |
+
|
6 |
+
# Lade das Modell
|
7 |
+
model_path = hf_hub_download(repo_id="foduucom/stockmarket-pattern-detection-yolov8", filename="model.pt")
|
8 |
+
model = YOLO(model_path)
|
9 |
+
|
10 |
+
def analyze_image(image):
|
11 |
+
# Führe Objekterkennung durch
|
12 |
+
results = model.predict(source=image, save=False)
|
13 |
+
# Extrahiere Ergebnisse
|
14 |
+
detections = []
|
15 |
+
for result in results:
|
16 |
+
for box in result.boxes:
|
17 |
+
label = result.names[int(box.cls)]
|
18 |
+
confidence = float(box.conf)
|
19 |
+
detections.append({
|
20 |
+
"pattern": label,
|
21 |
+
"confidence": confidence,
|
22 |
+
"color": "green" if "Bullish" in label else "red"
|
23 |
+
})
|
24 |
+
return detections
|
25 |
+
|
26 |
+
# Erstelle Gradio-Schnittstelle
|
27 |
+
iface = gr.Interface(
|
28 |
+
fn=analyze_image,
|
29 |
+
inputs=gr.Image(type="pil"),
|
30 |
+
outputs="json",
|
31 |
+
title="Candlestick Pattern Detection",
|
32 |
+
description="Upload a TradingView screenshot to detect candlestick patterns and colors."
|
33 |
+
)
|
34 |
+
|
35 |
+
# Starte die App
|
36 |
+
iface.launch()
|