init r1

computer_use_demo/gradio/agent/llm_utils/groqclient.py

@@ -0,0 +1,59 @@
+from groq import Groq
+import os
+from .utils import is_image_path
+
+def run_groq_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0.6):
+    """
+    Run a chat completion through Groq's API, ignoring any images in the messages.
+    """
+    api_key = api_key or os.environ.get("GROQ_API_KEY")
+    if not api_key:
+        raise ValueError("GROQ_API_KEY is not set")
+    
+    client = Groq(api_key=api_key)
+    # avoid using system messages for R1
+    final_messages = [{"role": "user", "content": system}]
+
+    if isinstance(messages, list):
+        for item in messages:
+            if isinstance(item, dict):
+                # For dict items, concatenate all text content, ignoring images
+                text_contents = []
+                for cnt in item["content"]:
+                    if isinstance(cnt, str):
+                        if not is_image_path(cnt):  # Skip image paths
+                            text_contents.append(cnt)
+                    else:
+                        text_contents.append(str(cnt))
+                
+                if text_contents:  # Only add if there's text content
+                    message = {"role": "user", "content": " ".join(text_contents)}
+                    final_messages.append(message)
+            else:  # str
+                message = {"role": "user", "content": item}
+                final_messages.append(message)
+    
+    elif isinstance(messages, str):
+        final_messages.append({"role": "user", "content": messages})
+
+    try:
+        completion = client.chat.completions.create(
+            model="deepseek-r1-distill-llama-70b",
+            messages=final_messages,
+            temperature=0.6,
+            max_completion_tokens=max_tokens,
+            top_p=0.95,
+            stream=False,
+            reasoning_format="raw"
+        )
+        
+        response = completion.choices[0].message.content
+        final_answer = response.split('</think>\n')[-1] if '</think>' in response else response
+        final_answer = final_answer.replace("<output>", "").replace("</output>", "")
+        token_usage = completion.usage.total_tokens
+        
+        return final_answer, token_usage
+    except Exception as e:
+        print(f"Error in interleaved Groq: {e}")
+
+        return str(e), 0

computer_use_demo/gradio/agent/llm_utils/oai.py

@@ -2,21 +2,9 @@ import os
 import logging
 import base64
 import requests
-
+from .utils import is_image_path, encode_image
-def is_image_path(text):
-    image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif")
-    if text.endswith(image_extensions):
-        return True
-    else:
-        return False
-
-def encode_image(image_path):
-    """Encode image file to base64."""
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode("utf-8")
 
 def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0):
-
     api_key = api_key or os.environ.get("OPENAI_API_KEY")
     if not api_key:
         raise ValueError("OPENAI_API_KEY is not set")
@@ -54,8 +42,6 @@ def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max
     elif isinstance(messages, str):
         final_messages = [{"role": "user", "content": messages}]
 
-    print("[oai] sending messages:", {"role": "user", "content": messages})
-
     payload = {
         "model": llm,
         "messages": final_messages,

computer_use_demo/gradio/agent/llm_utils/omniparserclient.py

@@ -2,7 +2,7 @@ import requests
 import base64
 from pathlib import Path
 from tools.screen_capture import get_screenshot
-from agent.llm_utils.oai import encode_image
+from agent.llm_utils.utils import encode_image
 
 OUTPUT_DIR = "./tmp/outputs"
 

computer_use_demo/gradio/agent/llm_utils/utils.py

@@ -0,0 +1,13 @@
+import base64
+
+def is_image_path(text):
+    image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif")
+    if text.endswith(image_extensions):
+        return True
+    else:
+        return False
+
+def encode_image(image_path):
+    """Encode image file to base64."""
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode("utf-8")

computer_use_demo/gradio/agent/vlm_agent.py

@@ -11,6 +11,7 @@ from anthropic.types import ToolResultBlockParam
 from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage
 
 from agent.llm_utils.oai import run_oai_interleaved
+from agent.llm_utils.groqclient import run_groq_interleaved
 import time
 import re
 
@@ -39,9 +40,12 @@ class VLMAgent:
     ):
         if model == "omniparser + gpt-4o":
             self.model = "gpt-4o-2024-11-20"
+        elif model == "omniparser + R1":
+            self.model = "deepseek-r1-distill-llama-70b"
         else:
             raise ValueError(f"Model {model} not supported")
         
+
         self.provider = provider
         self.api_key = api_key
         self.api_response_callback = api_response_callback
@@ -108,8 +112,17 @@ class VLMAgent:
             print(f"oai token usage: {token_usage}")
             self.total_token_usage += token_usage
             self.total_cost += (token_usage * 0.15 / 1000000)  # https://openai.com/api/pricing/
-        elif "phi" in self.model:
+        elif "r1" in self.model:
-            pass # TODO
+            vlm_response, token_usage = run_groq_interleaved(
+                messages=planner_messages,
+                system=system,
+                llm=self.model,
+                api_key=self.api_key,
+                max_tokens=self.max_tokens,
+            )
+            print(f"groq token usage: {token_usage}")
+            self.total_token_usage += token_usage
+            self.total_cost += (token_usage * 0.99 / 1000000)
         else:
             raise ValueError(f"Model {self.model} not supported")
         latency_vlm = time.time() - start
@@ -168,7 +181,7 @@ class VLMAgent:
         self.api_response_callback(response)
 
     def _get_system_prompt(self, screen_info: str = ""):
-        return f"""
+        main_section = f"""
 You are using a Windows device.
 You are able to use a mouse and keyboard to interact with the computer based on the given task and screenshot.
 You can only interact with the desktop GUI (no terminal or application menu access).
@@ -222,11 +235,27 @@ Another Example:
 
 IMPORTANT NOTES:
 1. You should only give a single action at a time.
+
+"""
+        thinking_model = "r1" in self.model
+        if not thinking_model:
+            main_section += """
 2. You should give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task.
+
+"""
+        else:
+            main_section += """
+2. In <think> XML tags give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task. In <output> XML tags put the next action prediction JSON.
+
+"""
+        main_section += """
 3. Attach the next action prediction in the "Next Action".
 4. You should not include other actions, such as keyboard shortcuts.
 5. When the task is completed, you should say "Next Action": "None" in the json field.
 """ 
+
+        return main_section
+
     def draw_action(self, vlm_response_json, image_base64):
         # draw a circle using the coordinate in parsed_screen['som_image_base64']
         image_data = base64.b64decode(image_base64)

computer_use_demo/gradio/app.py

@@ -28,12 +28,13 @@ API_KEY_FILE = CONFIG_DIR / "api_key"
 INTRO_TEXT = '''
 🚀🤖✨ It's Play Time!
 
-Welcome to the OmniParser+X Computer Use Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent.
+Welcome to the OmniParser+X Computer Use Demo! X = [GPT-4o, R1, Claude]. Let OmniParser turn your general purpose vision-langauge model to an AI agent.
 
 Type a message and press submit to start OmniParser+X. Press the trash icon in the chat to clear the message history.
 '''
 
 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")
@@ -255,11 +256,10 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
         }
         </style>
     """)
-    state = gr.State({})  # Use Gradio's state management
+    state = gr.State({})
 
-    setup_state(state.value)  # Initialize the state
+    setup_state(state.value)
-
+    
-    # Retrieve screen details
     gr.Markdown("# OmniParser + ✖️ Demo")
 
     if not os.getenv("HIDE_WARNING", False):
@@ -270,8 +270,8 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
             with gr.Column():
                 model = gr.Dropdown(
                     label="Model",
-                    choices=["omniparser + gpt-4o", "omniparser + phi35v", "claude-3-5-sonnet-20241022"],
+                    choices=["omniparser + gpt-4o", "omniparser + R1", "claude-3-5-sonnet-20241022"],
-                    value="omniparser + gpt-4o",  # Set to one of the choices
+                    value="omniparser + gpt-4o",
                     interactive=True,
                 )
             with gr.Column():
@@ -322,11 +322,14 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
         
         if model_selection == "claude-3-5-sonnet-20241022":
             provider_choices = [option.value for option in APIProvider if option.value != "openai"]
-        elif model_selection == "omniparser + gpt-4o" or model_selection == "omniparser + phi35v":
+        elif model_selection == "omniparser + gpt-4o":
             provider_choices = ["openai"]
+        elif model_selection == "omniparser + R1":
+            provider_choices = ["groq"]
         else:
             provider_choices = [option.value for option in APIProvider]
         default_provider_value = provider_choices[0]
+
         provider_interactive = len(provider_choices) > 1
         api_key_placeholder = f"{default_provider_value.title()} API Key"
 

requirements.txt

@@ -28,4 +28,5 @@ boto3>=1.28.57
 google-auth<3,>=2
 screeninfo
 uiautomation
-dashscope
+dashscope
+groq