Update app.py

app.py

@@ -118,11 +118,11 @@ huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
 # )
 
 
-hf_hub_download(
-    repo_id="unsloth/Qwen3-VL-32B-Thinking-GGUF",
-    filename="Qwen3-VL-32B-Thinking-Q8_0.gguf",
-    local_dir="./models"
-)
+# hf_hub_download(
+#     repo_id="unsloth/Qwen3-VL-32B-Thinking-GGUF",
+#     filename="Qwen3-VL-32B-Thinking-Q8_0.gguf",
+#     local_dir="./models"
+# )
 
 
 # hf_hub_download(
@@ -236,128 +236,6 @@ llm_model_qwen= None
 
 
 
-_IMAGE_MIME_TYPES = {
-    # Most common formats
-    '.png':  'image/png',
-    '.jpg':  'image/jpeg',
-    '.jpeg': 'image/jpeg',
-    '.gif':  'image/gif',
-    '.webp': 'image/webp',
-
-    # Next-generation formats
-    '.avif': 'image/avif',
-    '.jp2':  'image/jp2',
-    '.j2k':  'image/jp2',
-    '.jpx':  'image/jp2',
-
-    # Legacy / Windows formats
-    '.bmp':  'image/bmp',
-    '.ico':  'image/x-icon',
-    '.pcx':  'image/x-pcx',
-    '.tga':  'image/x-tga',
-    '.icns': 'image/icns',
-
-    # Professional / Scientific imaging
-    '.tif':  'image/tiff',
-    '.tiff': 'image/tiff',
-    '.eps':  'application/postscript',
-    '.dds':  'image/vnd-ms.dds',
-    '.dib':  'image/dib',
-    '.sgi':  'image/sgi',
-
-    # Portable Map formats (PPM/PGM/PBM)
-    '.pbm':  'image/x-portable-bitmap',
-    '.pgm':  'image/x-portable-graymap',
-    '.ppm':  'image/x-portable-pixmap',
-
-    # Miscellaneous / Older formats
-    '.xbm':  'image/x-xbitmap',
-    '.mpo':  'image/mpo',
-    '.msp':  'image/msp',
-    '.im':   'image/x-pillow-im',
-    '.qoi':  'image/qoi',
-}
-
-def image_to_base64_data_uri(
-    file_path: str,
-    *,
-    fallback_mime: str = "application/octet-stream"
-) -> str:
-    """
-    Convert a local image file to a base64-encoded data URI with the correct MIME type.
-
-    Supports 20+ image formats (PNG, JPEG, WebP, AVIF, BMP, ICO, TIFF, etc.).
-
-    Args:
-        file_path: Path to the image file on disk.
-        fallback_mime: MIME type used when the file extension is unknown.
-
-    Returns:
-        A valid data URI string (e.g., data:image/webp;base64,...).
-
-    Raises:
-        FileNotFoundError: If the file does not exist.
-        OSError: If reading the file fails.
-    """
-    if not os.path.isfile(file_path):
-        raise FileNotFoundError(f"Image file not found: {file_path}")
-
-    extension = os.path.splitext(file_path)[1].lower()
-    mime_type = _IMAGE_MIME_TYPES.get(extension, fallback_mime)
-
-    if mime_type == fallback_mime:
-        print(f"Warning: Unknown extension '{extension}' for '{file_path}'. "
-              f"Using fallback MIME type: {fallback_mime}")
-
-    try:
-        with open(file_path, "rb") as img_file:
-            encoded_data = base64.b64encode(img_file.read()).decode("utf-8")
-    except OSError as e:
-        raise OSError(f"Failed to read image file '{file_path}': {e}") from e
-
-    return f"data:{mime_type};base64,{encoded_data}"
-
-
-
-######################   sample code ################################################
-# --- Main Logic for Image Processing ---
-
-# # 1. Create a list containing all image paths
-# image_paths = [
-#     r'./scene.jpeg',
-#     r'./cat.png',
-#     r'./network.webp',
-#     # Add more image paths here if needed
-# ]
-
-# # 2. Create an empty list to store the message objects (images and text)
-# images_messages = []
-
-
-
-# # 3. Loop through the image path list, convert each image to a Data URI,
-# #    and add it to the message list as an image_url object.
-# for path in image_paths:
-#     data_uri = image_to_base64_data_uri(path)
-#     images_messages.append({"type": "image_url", "image_url": {"url": data_uri}})
-
-# # 4. Add the final text prompt at the end of the list
-# images_messages.append({"type": "text", "text": "Describes the images."})
-
-# # 5. Use this list to build the chat_completion request
-# res = llm.create_chat_completion(
-#     messages=[
-#         {"role": "system", "content": "You are a highly accurate vision-language assistant. Provide detailed, precise, and well-structured image descriptions."},
-#         # The user's content is the list containing both images and text
-#         {"role": "user", "content": images_messages}
-#     ]
-# )
-
-# # Print the assistant's response
-# print(res["choices"][0]["message"]["content"])
-
-
-
 
 @spaces.GPU(duration=120)
 def respond(