mod

app.py

@@ -1,6 +1,22 @@
+import os
 import gradio as gr
-import spaces
-from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+
+# --- Patch gradio_client boolean-schema bug ---
+import gradio_client.utils as gcu
+
+orig_json_schema_to_python_type = gcu._json_schema_to_python_type
+
+def _safe_json_schema_to_python_type(schema, defs):
+    # Fix: handle boolean schema values for additionalProperties
+    if isinstance(schema, bool):
+        # True → any type allowed; False → never allowed
+        return "Any" if schema else "Never"
+    return orig_json_schema_to_python_type(schema, defs)
+
+gcu._json_schema_to_python_type = _safe_json_schema_to_python_type
+# ------------------------------------------------
+
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
 from qwen_vl_utils import process_vision_info
 import torch
 import base64
@@ -8,52 +24,60 @@ from PIL import Image, ImageDraw
 from io import BytesIO
 import re
 
+# -------- Runtime / device --------
+# Force CPU usage
+device = "cpu"
+
+# Hugging Face Spaces port
+PORT = int(os.getenv("PORT", "7860"))
 
+# -------- Model / Processor --------
+# NOTE: device_map=None + .to(device) keeps everything on CPU
 models = {
-    "OS-Copilot/OS-Atlas-Base-7B": Qwen2VLForConditionalGeneration.from_pretrained("OS-Copilot/OS-Atlas-Base-7B", torch_dtype="auto", device_map="auto"),
+    "OS-Copilot/OS-Atlas-Base-7B": Qwen2VLForConditionalGeneration.from_pretrained(
+        "OS-Copilot/OS-Atlas-Base-7B",
+        dtype="auto",          # use 'dtype' (new) rather than deprecated 'torch_dtype'
+        device_map=None
+    ).to(device)
 }
 
 processors = {
     "OS-Copilot/OS-Atlas-Base-7B": AutoProcessor.from_pretrained("OS-Copilot/OS-Atlas-Base-7B")
 }
 
-
-def image_to_base64(image):
+# -------- Helpers --------
+def image_to_base64(image: Image.Image) -> str:
     buffered = BytesIO()
     image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-    return img_str
+    return base64.b64encode(buffered.getvalue()).decode("utf-8")
 
-
-def draw_bounding_boxes(image, bounding_boxes, outline_color="red", line_width=2):
+def draw_bounding_boxes(image: Image.Image, bounding_boxes, outline_color="red", line_width=2):
     draw = ImageDraw.Draw(image)
-    for box in bounding_boxes:
+    for box in bounding_boxes or []:
         xmin, ymin, xmax, ymax = box
         draw.rectangle([xmin, ymin, xmax, ymax], outline=outline_color, width=line_width)
     return image
 
-
 def rescale_bounding_boxes(bounding_boxes, original_width, original_height, scaled_width=1000, scaled_height=1000):
+    if not bounding_boxes:
+        return []
     x_scale = original_width / scaled_width
     y_scale = original_height / scaled_height
-    rescaled_boxes = []
-    for box in bounding_boxes:
-        xmin, ymin, xmax, ymax = box
-        rescaled_box = [
-            xmin * x_scale,
-            ymin * y_scale,
-            xmax * x_scale,
-            ymax * y_scale
-        ]
-        rescaled_boxes.append(rescaled_box)
-    return rescaled_boxes
-
+    return [
+        [xmin * x_scale, ymin * y_scale, xmax * x_scale, ymax * y_scale]
+        for (xmin, ymin, xmax, ymax) in bounding_boxes
+    ]
 
-@spaces.GPU
+# -------- Inference --------
 def run_example(image, text_input, model_id="OS-Copilot/OS-Atlas-Base-7B"):
+    # Basic validation so the Space doesn't 500
+    if image is None or (text_input is None or str(text_input).strip() == ""):
+        return "", [], image
+
     model = models[model_id].eval()
     processor = processors[model_id]
-    prompt = f"In this UI screenshot, what is the position of the element corresponding to the command \"{text_input}\" (with bbox)?"
+
+    prompt = f'In this UI screenshot, what is the position of the element corresponding to the command "{text_input}" (with bbox)?'
     messages = [
         {
             "role": "user",
@@ -64,9 +88,8 @@ def run_example(image, text_input, model_id="OS-Copilot/OS-Atlas-Base-7B"):
         }
     ]
 
-    text = processor.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
-    )
+    # Build inputs
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
     image_inputs, video_inputs = process_vision_info(messages)
     inputs = processor(
         text=[text],
@@ -75,43 +98,70 @@ def run_example(image, text_input, model_id="OS-Copilot/OS-Atlas-Base-7B"):
         padding=True,
         return_tensors="pt",
     )
-    inputs = inputs.to("cuda")
 
-    generated_ids = model.generate(**inputs, max_new_tokens=128)
-    generated_ids_trimmed = [
-        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-    ]
-    output_text = processor.batch_decode(
+    # Move tensors to CPU explicitly
+    inputs = {k: (v.to(device) if hasattr(v, "to") else v) for k, v in inputs.items()}
+
+    # Generate
+    with torch.no_grad():
+        generated_ids = model.generate(**inputs, max_new_tokens=128)
+
+    # Post-process
+    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs["input_ids"], generated_ids)]
+    output_texts = processor.batch_decode(
         generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
     )
-    print(output_text)
-    text = output_text[0]
+    text = output_texts[0] if output_texts else ""
 
+    # Parse object_ref and bbox defensively
     object_ref_pattern = r"<\|object_ref_start\|>(.*?)<\|object_ref_end\|>"
     box_pattern = r"<\|box_start\|>(.*?)<\|box_end\|>"
 
-    object_ref = re.search(object_ref_pattern, text).group(1)
-    box_content = re.search(box_pattern, text).group(1)
+    object_match = re.search(object_ref_pattern, text or "")
+    box_match = re.search(box_pattern, text or "")
 
-    boxes = [tuple(map(int, pair.strip("()").split(','))) for pair in box_content.split("),(")]
-    boxes = [[boxes[0][0], boxes[0][1], boxes[1][0], boxes[1][1]]]
+    object_ref = object_match.group(1).strip() if object_match else ""
+    box_content = box_match.group(1).strip() if box_match else ""
 
-    scaled_boxes = rescale_bounding_boxes(boxes, image.width, image.height)
-    return object_ref, text, draw_bounding_boxes(image, scaled_boxes)
+    boxes = []
+    if box_content:
+        try:
+            # Expecting "(x1,y1),(x2,y2)" -> convert to [xmin, ymin, xmax, ymax]
+            parts = [p.strip() for p in box_content.split("),(")]
+            parts[0] = parts[0].lstrip("(")
+            parts[-1] = parts[-1].rstrip(")")
+            coords = [tuple(map(int, p.split(","))) for p in parts]
+            if len(coords) >= 2:
+                (x1, y1), (x2, y2) = coords[0], coords[1]
+                boxes = [[x1, y1, x2, y2]]
+        except Exception:
+            boxes = []
 
+    scaled_boxes = rescale_bounding_boxes(boxes, image.width, image.height) if boxes else []
+    annotated = draw_bounding_boxes(image.copy(), scaled_boxes) if scaled_boxes else image
+
+    return object_ref, scaled_boxes, annotated
+
+# -------- UI --------
 css = """
   #output {
-    height: 500px; 
-    overflow: auto; 
-    border: 1px solid #ccc; 
+    height: 500px;
+    overflow: auto;
+    border: 1px solid #ccc;
   }
 """
-with gr.Blocks() as demo:
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# Demo for OS-ATLAS: A Foundation Action Model For Generalist GUI Agents")
+
     with gr.Row():
-        gr.HTML(f"<style>{css}</style>")
         with gr.Column():
             input_img = gr.Image(label="Input Image", type="pil")
-            model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="OS-Copilot/OS-Atlas-Base-7B")
+            model_selector = gr.Dropdown(
+                choices=list(models.keys()),
+                label="Model",
+                value="OS-Copilot/OS-Atlas-Base-7B"
+            )
             text_input = gr.Textbox(label="User Prompt")
             submit_btn = gr.Button(value="Submit")
         with gr.Column():
@@ -125,12 +175,34 @@ with gr.Blocks() as demo:
             ["assets/web_6f93090a-81f6-489e-bb35-1a2838b18c01.png", "switch to discussions"],
         ],
         inputs=[input_img, text_input],
-        outputs=[model_output_text, model_output_box, annotated_image],
-        fn=run_example,
-        cache_examples=False,
-        label="Try examples"
+        # remove fn/outputs so examples only prefill inputs
+    )
+
+    submit_btn.click(
+        run_example,
+        [input_img, text_input, model_selector],
+        [model_output_text, model_output_box, annotated_image],
     )
 
-    submit_btn.click(run_example, [input_img, text_input, model_selector], [model_output_text, model_output_box, annotated_image])
+# ---- Make Gradio/Starlette error responses small & safe (no Content-Length drama) ----
+from fastapi import Request
+from starlette.responses import PlainTextResponse
+
+app = demo.app  # FastAPI app behind Gradio Blocks
+
+@app.exception_handler(Exception)
+async def _catch_all_exceptions(request: Request, exc: Exception):
+    # Return a very small body so Starlette/Uvicorn never miscounts bytes
+    return PlainTextResponse("Internal Server Error", status_code=500)
+# --------------------------------------------------------------------------------------
+
 
-demo.launch(debug=True)
+# -------- Launch (Spaces-friendly) --------
+demo.queue().launch(
+    server_name="0.0.0.0",
+    server_port=PORT,
+    show_error=False,   # avoid large HTML error bodies
+    debug=False,        # avoid big pretty tracebacks (and Content-Length mismatch)
+    show_api=False      # <— key: disables /api/info schema generation
+    # api_open=False    # if your Gradio version expects the old name, use this instead of show_api
+)