supprt local data logging

README.md

@@ -3,6 +3,7 @@
 <p align="center">
   <img src="imgs/logo.png" alt="Logo">
 </p>
+<!-- <a href="https://trendshift.io/repositories/12975" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12975" alt="microsoft%2FOmniParser | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> -->
 
 [![arXiv](https://img.shields.io/badge/Paper-green)](https://arxiv.org/abs/2408.00203)
 [![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
@@ -12,6 +13,7 @@
 **OmniParser** is a comprehensive method for parsing user interface screenshots into structured and easy-to-understand elements, which significantly enhances the ability of GPT-4V to generate actions that can be accurately grounded in the corresponding regions of the interface. 
 
 ## News
+- [2025/3] We support local logging of trajecotry so that you can use OmniParser+OmniTool to build training data pipeline for your favorate agent in your domain. [Documentation WIP]
 - [2025/3] We are gradually adding multi agents orchstration and improving user interface in OmniTool for better experience.
 - [2025/2] We release OmniParser V2 [checkpoints](https://huggingface.co/microsoft/OmniParser-v2.0). [Watch Video](https://1drv.ms/v/c/650b027c18d5a573/EWXbVESKWo9Buu6OYCwg06wBeoM97C6EOTG6RjvWLEN1Qg?e=alnHGC)
 - [2025/2] We introduce OmniTool: Control a Windows 11 VM with OmniParser + your vision model of choice. OmniTool supports out of the box the following large language models - OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use. [Watch Video](https://1drv.ms/v/c/650b027c18d5a573/EehZ7RzY69ZHn-MeQHrnnR4BCj3by-cLLpUVlxMjF4O65Q?e=8LxMgX)

omnitool/gradio/agent/vlm_agent_with_orchestrator.py

@@ -17,7 +17,7 @@ from agent.llm_utils.groqclient import run_groq_interleaved
 from agent.llm_utils.utils import is_image_path
 import time
 import re
-
+import os
 OUTPUT_DIR = "./tmp/outputs"
 ORCHESTRATOR_LEDGER_PROMPT = """
 Recall we are working on the following request:
@@ -73,7 +73,7 @@ class VLMOrchestratedAgent:
         max_tokens: int = 4096,
         only_n_most_recent_images: int | None = None,
         print_usage: bool = True,
-        save_folder: str = "./uploads",
+        save_folder: str = None,
     ):
         if model == "omniparser + gpt-4o" or model == "omniparser + gpt-4o-orchestrated":
             self.model = "gpt-4o-2024-11-20"
@@ -95,22 +95,20 @@ class VLMOrchestratedAgent:
         self.max_tokens = max_tokens
         self.only_n_most_recent_images = only_n_most_recent_images
         self.output_callback = output_callback
-        self.save_folder = Path(save_folder).absolute()
+        self.save_folder = save_folder
-        
-        # Create save folder if it doesn't exist
-        self.save_folder.mkdir(parents=True, exist_ok=True)
         
         self.print_usage = print_usage
         self.total_token_usage = 0
         self.total_cost = 0
         self.step_count = 0
+        self.plan, self.ledger = None, None
 
         self.system = ''
     
     def __call__(self, messages: list, parsed_screen: list[str, list, dict]):
         if self.step_count == 0:
             plan = self._initialize_task(messages)
-            self.output_callback(f'-- Plan: {plan} --', sender="bot")
+            self.output_callback(f'-- Plan: {plan} --', )
             # update messages with the plan
             messages.append({"role": "assistant", "content": plan})
         else:
@@ -122,13 +120,18 @@ class VLMOrchestratedAgent:
                 f'    <pre>{updated_ledger}</pre>'
                 f'  </div>'
                 f'</details>',
-                sender="bot"
             )
             # update messages with the ledger
             messages.append({"role": "assistant", "content": updated_ledger})
+            self.ledger = updated_ledger
 
         self.step_count += 1
-        image_base64 = parsed_screen['original_screenshot_base64']
+        # save the image to the output folder
+        with open(f"{self.save_folder}/screenshot_{self.step_count}.png", "wb") as f:
+            f.write(base64.b64decode(parsed_screen['original_screenshot_base64']))
+        with open(f"{self.save_folder}/som_screenshot_{self.step_count}.png", "wb") as f:
+            f.write(base64.b64decode(parsed_screen['som_image_base64']))
+
         latency_omniparser = parsed_screen['latency']
         screen_info = str(parsed_screen['screen_info'])
         screenshot_uuid = parsed_screen['screenshot_uuid']
@@ -196,7 +199,7 @@ class VLMOrchestratedAgent:
         latency_vlm = time.time() - start
         
         # Update step counter with both latencies
-        self.output_callback(f'<i>Step {self.step_count} | OmniParser: {latency_omniparser:.2f}s | LLM: {latency_vlm:.2f}s</i>', sender="bot")
+        self.output_callback(f'<i>Step {self.step_count} | OmniParser: {latency_omniparser:.2f}s | LLM: {latency_vlm:.2f}s</i>', )
 
         print(f"{vlm_response}")
         
@@ -226,7 +229,7 @@ class VLMOrchestratedAgent:
             except:
                 print(f"Error parsing: {vlm_response_json}")
                 pass
-        self.output_callback(f'<img src="data:image/png;base64,{img_to_show_base64}">', sender="bot")
+        self.output_callback(f'<img src="data:image/png;base64,{img_to_show_base64}">', )
         
         # Display screen info in a collapsible dropdown
         self.output_callback(
@@ -236,7 +239,6 @@ class VLMOrchestratedAgent:
             f'    <pre>{screen_info}</pre>'
             f'  </div>'
             f'</details>',
-            sender="bot"
         )
         
         vlm_plan_str = ""
@@ -267,6 +269,21 @@ class VLMOrchestratedAgent:
                                             name='computer', type='tool_use')
             response_content.append(sim_content_block)
         response_message = BetaMessage(id=f'toolu_{uuid.uuid4()}', content=response_content, model='', role='assistant', type='message', stop_reason='tool_use', usage=BetaUsage(input_tokens=0, output_tokens=0))
+
+        # save the intermediate step trajectory to the save folder
+        step_trajectory = {
+            "screenshot_path": f"{self.save_folder}/screenshot_{self.step_count}.png",
+            "som_screenshot_path": f"{self.save_folder}/som_screenshot_{self.step_count}.png",
+            "screen_info": screen_info,
+            "latency_omniparser": latency_omniparser,
+            "latency_vlm": latency_vlm,
+            "vlm_response_json": vlm_response_json,
+            'ledger': self.ledger,
+        }
+        with open(f"{self.save_folder}/trajectory.json", "a") as f:
+            f.write(json.dumps(step_trajectory))
+            f.write("\n")
+
         return response_message, vlm_response_json
 
     def _api_response_callback(self, response: APIResponse):
@@ -376,9 +393,8 @@ IMPORTANT NOTES:
         plan = extract_data(vlm_response, "json")
         
         # Create a filename with timestamp
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        plan_filename = f"plan.json"
-        plan_filename = f"plan_{timestamp}.json"
+        plan_path = os.path.join(self.save_folder, plan_filename)
-        plan_path = self.save_folder / plan_filename
         
         # Save the plan to a file
         try:

omnitool/gradio/app_new.py

@@ -1,4 +1,7 @@
 """
+The app contains:
+- a new UI for the OmniParser AI Agent.
+- 
 python app_new.py --windows_host_url localhost:8006 --omniparser_server_url localhost:8000
 """
 
@@ -28,10 +31,6 @@ import base64
 
 CONFIG_DIR = Path("~/.anthropic").expanduser()
 API_KEY_FILE = CONFIG_DIR / "api_key"
-UPLOAD_FOLDER = Path("./uploads").absolute()
-
-# Create uploads directory if it doesn't exist
-UPLOAD_FOLDER.mkdir(parents=True, exist_ok=True)
 
 INTRO_TEXT = '''
 <div style="text-align: center; margin-bottom: 10px;">
@@ -46,13 +45,13 @@ def parse_arguments():
     parser = argparse.ArgumentParser(description="Gradio App")
     parser.add_argument("--windows_host_url", type=str, default='localhost:8006')
     parser.add_argument("--omniparser_server_url", type=str, default="localhost:8000")
-    parser.add_argument("--upload_folder", type=str, default="./uploads")
+    parser.add_argument("--run_folder", type=str, default="./tmp/outputs")
     return parser.parse_args()
 args = parse_arguments()
 
 # Update upload folder from args if provided
-UPLOAD_FOLDER = Path(args.upload_folder).absolute()
+RUN_FOLDER = Path(os.path.join(args.run_folder, datetime.now().strftime('%Y%m%d_%H%M')))
-UPLOAD_FOLDER.mkdir(parents=True, exist_ok=True)
+RUN_FOLDER.mkdir(parents=True, exist_ok=True)
 
 class Sender(StrEnum):
     USER = "user"
@@ -63,8 +62,8 @@ class Sender(StrEnum):
 def load_existing_files():
     """Load all existing files from the uploads folder"""
     files = []
-    if UPLOAD_FOLDER.exists():
+    if RUN_FOLDER.exists():
-        for file_path in UPLOAD_FOLDER.iterdir():
+        for file_path in RUN_FOLDER.iterdir():
             if file_path.is_file():
                 files.append(str(file_path))
     return files
@@ -277,7 +276,7 @@ def process_input(user_input, state):
         only_n_most_recent_images=state["only_n_most_recent_images"],
         max_tokens=16384,
         omniparser_url=args.omniparser_server_url,
-        save_folder=str(UPLOAD_FOLDER)
+        save_folder=str(RUN_FOLDER)
     ):  
         if loop_msg is None or state.get("stop"):
             # Detect and add new files to the state
@@ -434,7 +433,7 @@ def handle_file_upload(files, state):
     for file in files:
         # Get the file name and create a path in the upload directory
         file_name = Path(file.name).name
-        file_path = UPLOAD_FOLDER / file_name
+        file_path = RUN_FOLDER / file_name
         
         # Save the file
         shutil.copy(file.name, file_path)
@@ -471,9 +470,9 @@ def toggle_view(view_mode, file_path=None, state=None):
 def detect_new_files(state):
     """Detect new files in the uploads folder and add them to the state"""
     new_files_count = 0
-    if UPLOAD_FOLDER.exists():
+    if RUN_FOLDER.exists():
         current_files = set(state['uploaded_files'])
-        for file_path in UPLOAD_FOLDER.iterdir():
+        for file_path in RUN_FOLDER.iterdir():
             if file_path.is_file():
                 file_path_str = str(file_path)
                 if file_path_str not in current_files: