add claude support

Author: roywei

operate/config.py

@@ -2,6 +2,7 @@
 import sys
 from dotenv import load_dotenv
 from openai import OpenAI
+import anthropic
 from prompt_toolkit.shortcuts import input_dialog
 import google.generativeai as genai
 
@@ -33,6 +34,10 @@ def __init__(self):
  self.google_api_key = (
  None # instance variables are backups in case saving to a `.env` fails
  )
+ self.anthropic_api_key = (
+ None # instance variables are backups in case saving to a `.env` fails
+ )
+
 
  def initialize_openai(self):
  if self.verbose:
@@ -71,6 +76,14 @@ def initialize_google(self):
  model = genai.GenerativeModel("gemini-pro-vision")
 
  return model
+ 
+ def initialize_anthropic(self):
+ if self.anthropic_api_key:
+ api_key = self.anthropic_api_key
+ else:
+ api_key = os.getenv("ANTHROPIC_API_KEY")
+ return anthropic.Anthropic(api_key=api_key)
+
 
  def validation(self, model, voice_mode):
  """
@@ -87,6 +100,9 @@ def validation(self, model, voice_mode):
  self.require_api_key(
  "GOOGLE_API_KEY", "Google API key", model == "gemini-pro-vision"
  )
+ self.require_api_key(
+ "ANTHROPIC_API_KEY", "Anthropic API key", model == "claude-3-with-ocr"
+ )
 
  def require_api_key(self, key_name, key_description, is_required):
  key_exists = bool(os.environ.get(key_name))

operate/models/apis.py

@@ -48,14 +48,16 @@ async def get_next_action(model, messages, objective, session_id):
  if model == "gpt-4-with-ocr":
  operation = await call_gpt_4_vision_preview_ocr(messages, objective, model)
  return operation, None
- elif model == "agent-1":
+ if model == "agent-1":
  return "coming soon"
- elif model == "gemini-pro-vision":
+ if model == "gemini-pro-vision":
  return call_gemini_pro_vision(messages, objective), None
- elif model == "llava":
- operation = call_ollama_llava(messages), None
- return operation
-
+ if model == "llava":
+ operation = call_ollama_llava(messages)
+ return operation, None
+ if model == "claude-3-with-ocr":
+ operation = await call_claude_3_with_ocr(messages, objective, model)
+ return operation, None
  raise ModelNotRecognizedException(model)
 
 
@@ -261,7 +263,7 @@ async def call_gpt_4_vision_preview_ocr(messages, objective, model):
  result = reader.readtext(screenshot_filename)
 
  text_element_index = get_text_element(
- result, text_to_click, screenshot_filename
+ result, text_to_click[:3], screenshot_filename
  )
  coordinates = get_text_coordinates(
  result, text_element_index, screenshot_filename
@@ -528,6 +530,159 @@ def call_ollama_llava(messages):
  return call_ollama_llava(messages)
 
 
+async def call_claude_3_with_ocr(messages, objective, model):
+ if config.verbose:
+ print("[call_claude_3_with_ocr]")
+
+ try:
+ time.sleep(1)
+ client = config.initialize_anthropic()
+
+ confirm_system_prompt(messages, objective, model)
+ screenshots_dir = "screenshots"
+ if not os.path.exists(screenshots_dir):
+ os.makedirs(screenshots_dir)
+
+ screenshot_filename = os.path.join(screenshots_dir, "screenshot.png")
+ capture_screen_with_cursor(screenshot_filename)
+
+ with open(screenshot_filename, "rb") as img_file:
+ img = Image.open(img_file)
+ 
+ # Calculate the new dimensions while maintaining the aspect ratio
+ original_width, original_height = img.size
+ aspect_ratio = original_width / original_height
+ new_width = 2560 # Adjust this value to achieve the desired file size
+ new_height = int(new_width / aspect_ratio)
+ 
+ # Resize the image
+ img_resized = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
+ 
+ # Save the resized image to a BytesIO object
+ img_buffer = io.BytesIO()
+ img_resized.save(img_buffer, format='PNG')
+ img_buffer.seek(0)
+ 
+ # Encode the resized image as base64
+ img_data = base64.b64encode(img_buffer.getvalue()).decode("utf-8")
+
+ if len(messages) == 1:
+ user_prompt = get_user_first_message_prompt()
+ else:
+ user_prompt = get_user_prompt()
+
+ vision_message = {
+ "role": "user",
+ "content": [
+ {
+ "type": "image",
+ "source": {
+ "type": "base64",
+ "media_type": "image/png",
+ "data": img_data,
+ },
+ },
+ {"type": "text", "text": user_prompt + "**REMEMBER** Only output json format, do not append any other text."},
+ ],
+ }
+ messages.append(vision_message)
+
+ # anthropic api expect system prompt as an separate argument
+ response = client.messages.create(
+ model="claude-3-opus-20240229",
+ max_tokens=3000,
+ system=messages[0]["content"],
+ messages=messages[1:],
+ )
+
+ content = response.content[0].text
+ content = clean_json(content)
+ content_str = content
+ try:
+ content = json.loads(content)
+ except json.JSONDecodeError as e:
+ if config.verbose:
+ print(
+ f"{ANSI_GREEN}[Self-Operating Computer]{ANSI_RED}[Error] JSONDecodeError: {e} {ANSI_RESET}"
+ )
+ response = client.messages.create(
+ model="claude-3-opus-20240229",
+ max_tokens=3000,
+ system=f"This json string is not valid, when using with json.loads(content) \
+ it throws the following error: {e}, return correct json string. **REMEMBER** Only output json format, do not append any other text.",
+ messages=[{"role": "user", "content": content}],
+ )
+ content = response.content[0].text
+ content = clean_json(content)
+ content_str = content
+ content = json.loads(content)
+
+ if config.verbose:
+ print(
+ f"{ANSI_GREEN}[Self-Operating Computer]{ANSI_BRIGHT_MAGENTA}[{model}] content: {content} {ANSI_RESET}"
+ )
+ processed_content = []
+
+ for operation in content:
+ if operation.get("operation") == "click":
+ text_to_click = operation.get("text")
+ if config.verbose:
+ print(
+ "[call_claude_3_ocr][click] text_to_click",
+ text_to_click,
+ )
+ # Initialize EasyOCR Reader
+ reader = easyocr.Reader(["en"])
+
+ # Read the screenshot
+ result = reader.readtext(screenshot_filename)
+
+ text_element_index = get_text_element(
+ result, text_to_click[:3], screenshot_filename
+ )
+ coordinates = get_text_coordinates(
+ result, text_element_index, screenshot_filename
+ )
+
+ # add `coordinates`` to `content`
+ operation["x"] = coordinates["x"]
+ operation["y"] = coordinates["y"]
+
+ if config.verbose:
+ print(
+ "[call_claude_3_ocr][click] text_element_index",
+ text_element_index,
+ )
+ print(
+ "[call_claude_3_ocr][click] coordinates",
+ coordinates,
+ )
+ print(
+ "[call_claude_3_ocr][click] final operation",
+ operation,
+ )
+ processed_content.append(operation)
+
+ else:
+ processed_content.append(operation)
+
+
+ assistant_message = {"role": "assistant", "content": content_str}
+ messages.append(assistant_message)
+
+ return processed_content
+
+ except Exception as e:
+ print(
+ f"{ANSI_GREEN}[Self-Operating Computer]{ANSI_BRIGHT_MAGENTA}[{model}] That did not work. Trying another method {ANSI_RESET}"
+ )
+ if config.verbose:
+ print("[Self-Operating Computer][Operate] error", e)
+ traceback.print_exc()
+ raise Exception(e)
+ #return gpt_4_fallback(messages, objective, model)
+ 
+
 def get_last_assistant_message(messages):
  """
  Retrieve the last message from the assistant in the messages array.

operate/models/prompts.py

@@ -72,6 +72,7 @@
 From looking at the screen, the objective, and your previous actions, take the next best series of action. 
 
 You have 4 possible operation actions available to you. The `pyautogui` library will be used to execute your decision. Your output will be used in a `json.loads` loads statement.
+**REMEMBER** Only output json format, do not append any other text.
 
 1. click - Move mouse and click - We labeled the clickable elements with red bounding boxes and IDs. Label IDs are in the following format with `x` being a number: `~x`
 ```
@@ -238,6 +239,13 @@ def get_system_prompt(model, objective):
  os_search_str=os_search_str,
  operating_system=operating_system,
  )
+ elif model == "claude-3-with-ocr":
+ prompt = SYSTEM_PROMPT_OCR.format(
+ objective=objective,
+ cmd_string=cmd_string,
+ os_search_str=os_search_str,
+ operating_system=operating_system,
+ )
  else:
  prompt = SYSTEM_PROMPT_STANDARD.format(
  objective=objective,

requirements.txt

@@ -51,4 +51,5 @@ google-generativeai==0.3.0
 aiohttp==3.9.1
 ultralytics==8.0.227
 easyocr==1.7.1
-ollama==0.1.6
+ollama==0.1.6
+anthropic