Upload app.py

app.py

@@ -1,5 +1,6 @@
 import spaces
 import os
+import requests
 import gradio as gr
 import easyocr
 import numpy as np
@@ -21,6 +22,54 @@ from pipeline_flux_controlnet_inpaint import FluxControlNetInpaintingPipeline
 import huggingface_hub
 huggingface_hub.login(os.getenv('HF_TOKEN_FLUX'))
 
+
+def download_from_github(repo, filepath, destination, token):
+    """
+    Download a file from a private GitHub repository.
+    
+    Args:
+        repo (str): The GitHub repository in the format "owner/repo".
+        filepath (str): The path to the file in the repository.
+        destination (str): The local file path to save the downloaded file.
+        token (str): The GitHub Personal Access Token.
+    
+    Returns:
+        None
+    """
+    url = f"https://api.github.com/repos/{repo}/contents/{filepath}"
+    headers = {"Authorization": f"token {token}"}
+    response = requests.get(url, headers=headers)
+    
+    if response.status_code == 200:
+        file_content = response.json().get('content')
+        if file_content:
+            os.makedirs(os.path.dirname(destination), exist_ok=True)
+            with open(destination, 'wb') as f:
+                f.write(base64.b64decode(file_content))
+            print(f"Downloaded {filepath} to {destination}")
+        else:
+            raise Exception(f"File content not found for {filepath} in {repo}")
+    else:
+        raise Exception(f"Failed to download {filepath} from {repo}. Status code: {response.status_code}")
+
+# Example: Define GitHub repo and file paths
+repo_name = "arj7192/NativDemoLocal"
+github_files = {
+    "speech_bubble_model.pt": "speech_bubble_model.pt",
+    "craft_mlt_25k.pth": "craft_mlt_25k.pth",
+    "english_g2.pth": "english_g2.pth",
+    "korean_g2.pth": "korean_g2.pth",
+    "latin_g2.pth": "latin_g2.pth",
+    "zh_sim_g2.pth": "zh_sim_g2.pth",
+}
+
+token = os.getenv("HF_GITHUB_TOKEN")
+
+# Download each file
+for filename, repo_path in github_files.items():
+    destination_path = f"{filename}"
+    download_from_github(repo_name, repo_path, destination_path, token)
+
 bubble_detection_model = YOLO("speech_bubble_model.pt")
 
 language_to_ocr = {
@@ -102,12 +151,9 @@ def localize_boxes(merged_results, img_boxes, source_language, target_language):
     print(merged_results)
     
     prompt = f"""You are an expert translator and localization specialist with deep understanding of both {source_language} and {target_language} cultures.
-
     Task: Translate the detected text while preserving the cultural context and maintaining visual harmony. Make the results in capital letters.
-
     Source Text and Coordinates:
     {merged_results}
-
     Requirements:
     1. Maintain the original meaning and tone while adapting to {target_language} cultural context
     2. Keep translations concise and visually balanced (similar character length when possible)
@@ -119,10 +165,8 @@ def localize_boxes(merged_results, img_boxes, source_language, target_language):
     4. Consider the visual context from the provided image
     5. Use appropriate formality level for {target_language}
     6. Maintain any special formatting (if present)
-
     Format your response EXACTLY as a JSON-like list of dictionaries. Keep the box coordinates EXACTLY as they are, do not change them, only translate the text.
     [{{'box': [[x0, y0], [x1, y0], [x1, y1], [x0, y1]], 'text': 'translated_text'}}]
-
     Important: Only output the JSON format above, no explanations or additional text."""
 
     client = OpenAI(api_key=OPENAI_API_KEY)