Code Parsing Error Fix

python3 main.py TOOL CODE: from smolagents import Tool from huggingface_hub import InferenceClient class TextToImageTool(Tool): description = "This tool creates an image according to a prompt, which is a text description." name = "image_generator" inputs = {"prompt": {"type": "string", "description": "The image generator prompt. Don't hesit ate to add details in the prompt to make the image look better, like 'high-res, photorealistic', etc."}} output_type = "image" model_sdxl = "black-forest-labs/FLUX.1-schnell" client = InferenceClient(model_sdxl) def forward(self, prompt): return self.client.text_to_image(prompt) ╭─────────────────────────────────────────── New run ────────────────────────────────────────────╮ │ │ │ Research and plan a day in Austin for 02/13/2025 │ │ │ ╰─ OllamaModel - ───────────────────────────────────────────────────────────────────────────────╯ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Error in code parsing: The code blob is invalid, because the regex pattern ```(?:py|python)?\n(.*?)\n``` was not found in code_blob='<think>\nAlright, so I need to help plan a day in Austin on February 13, 2025. Let me think about how to approach this.\n\nFirst, I should consider the weather. Austin in mid-February is usually mild, maybe around 50-70°F. That’s good because it means outdoor activities are feasible without extreme heat or cold.\n\nNext, what does a typical day trip consist of? Usually, people like a mix of sightseeing, dining, and some unique local experiences. So I should break the day into morning, afternoon, and evening segments.\n\nFor breakfast, I need a popular spot that’s both tasty and gives a good Austin vibe. Café No Sé comes to mind because it\'s trendy and has a nice ambiance. Their menu is also pretty appealing for a morning start.\n\nAfter breakfast, exploring the city makes sense. The Texas State Capitol is an iconic spot and free to tour, which is great. Then, maybe some street art since Austin is known for its murals. The "I love you so much" mural at Jo\'s Coffee is a classic and a good photo op.\n\nLunch should be something quintessentially Austin—barbecue! Franklin Barbecue is famous but often has long lines. Alternatively, La Barbecue might be a good backup if Franklin’s too busy.\n\nIn the afternoon, South Congress Avenue (SoCo) offers a variety of shops and galleries. It\'s a great place to walk around, shop, and enjoy the local culture. Maybe include a suggestion for dessert here, like Amy’s Ice Creams or Home Slice Pizza for something savory.\n\nDinner should be at a nice restaurant. Uchi is a well-known spot with excellent reviews, offering a modern take on Japanese cuisine. It\'s a bit pricier but worth it for the experience.\n\nFor the evening, live music is a must in Austin. The Continental Club is legendary and usually has great acts playing. Ending the day with some live tunes would cap off the trip nicely.\n\nI should also think about transportation. Since some locations are spread out, maybe mention ride-sharing or renting a bike as options. Also, check if any of these spots have specific hours or require reservations, especially for dinner at Uchi and possibly Franklin Barbecue.\n\nWait, I should verify if all these places will be open on February 13, 2025. That’s a Thursday, so most should be operational unless there are any special events that day. Maybe suggest checking for any local happenings or festivals that could affect plans.\n\nAlso, considering the timing: breakfast around 8-9 AM, then exploring until lunch at 11:30 AM. Afternoon activities from 1 PM to 4 PM, dinner at 6 PM, and live music starting at 8 PM.\n\nI think this covers a balanced day with food, culture, and entertainment without being too rushed. Maybe add some alternatives in case any spots are crowded or closed.\n</think>\n\n**Plan for a Day in Austin on February 13, 2025**\n\n**Morning:**\n- **Breakfast:** Start your day at Café No Sé (9th Ave) around 8-9 AM. Enjoy their trendy ambiance and delicious menu offerings.\n\n**Mid-Morning Exploration:**\n- **Texas State Capitol Tour:** Head over for a free tour, a great way to immerse yourself in Texas history.\n- **Street Art:** Visit the "I love you so much" mural at Jo\'s Coffee on South Congress Avenue for a classic photo opportunity.\n\n**Lunch:**\n- **Barbecue Experience:** Aim for lunch around 11:30 AM. Try Franklin Barbecue, but be prepared for lines. La Barbecue is a good alternative if needed.\n\n**Afternoon:**\n- **South Congress Avenue (SoCo):** Explore boutique shops and galleries from 1 PM to 4 PM. Consider stopping at Amy’s Ice Creams or Home Slice Pizza for a treat.\n\n**Dinner:**\n- **Evening Meal:** Dine at Uchi around 6 PM. This modern Japanese restaurant offers an exceptional culinary experience, so reservations might be wise.\n\n**Evening Entertainment:**\n- **Live Music:** End your day with live music at The Continental Club starting around 8 PM, a iconic venue showcasing great acts.\n\n**Transportation Tips:**\n- Use ride-sharing services or rent bikes to navigate between locations efficiently.\n\n**Additional Considerations:**\n- Check for any local events or festivals that might affect plans.\n- Verify the opening hours and reservation policies for each location before heading out.\n\nThis plan offers a balanced mix of Austin\'s culture, cuisine, and entertainment, ensuring a memorable day without feeling rushed.'. Make sure to include code with the correct pattern, for instance: Thoughts: Your thoughts Code: ```py # Your python code here ```<end_code> Make sure to provide correct code blobs. [Step 0: Duration 1800.19 seconds] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Error in code parsing: The code blob is invalid, because the regex pattern ```(?:py|python)?\n(.*?)\n``` was not found in code_blob='\n\n---\n\n**Plan for a Day in Austin on February 13, 2025**\n\n**Morning:**\n- **Breakfast:** Start your day at Café No Sé (9th Ave) around 8-9 AM. Enjoy their trendy ambiance and delicious menu offerings.\n\n**Mid-Morning Exploration:**\n- **Texas State Capitol Tour:** Head over for a free tour, a great way to immerse yourself in Texas history.\n- **Street Art:** Visit the "I love you so much" mural at Jo\'s Coffee on South Congress Avenue for a classic photo opportunity.\n\n**Lunch:**\n- **Barbecue Experience:** Aim for lunch around 11:30 AM. Try Franklin Barbecue, but be prepared for lines. La Barbecue is a good alternative if needed.\n\n**Afternoon:**\n- **South Congress Avenue (SoCo):** Explore boutique shops and galleries from 1 PM to 4 PM. Consider stopping at Amy’s Ice Creams or Home Slice Pizza for a treat.\n\n**Dinner:**\n- **Evening Meal:** Dine at Uchi around 6 PM. This modern Japanese restaurant offers an exceptional culinary experience, so reservations might be wise.\n\n**Evening Entertainment:**\n- **Live Music:** End your day with live music at The Continental Club starting around 8 PM, a iconic venue showcasing great acts.\n\n**Transportation Tips:**\n- Use ride-sharing services or rent bikes to navigate between locations efficiently.\n\n**Additional Considerations:**\n- Check for any local events or festivals that might affect plans.\n- Verify the opening hours and reservation policies for each location before heading out.\n\nThis plan offers a balanced mix of Austin\'s culture, cuisine, and entertainment, ensuring a memorable day without feeling rushed.'. Make sure to include code with the correct pattern, for instance: Thoughts: Your thoughts Code: ```py # Your python code here ```<end_code> Make sure to provide correct code blobs. [Step 1: Duration 187.64 seconds] ────────────────────────────────────────── Updated plan ────────────────────────────────────────── I still need to solve the task I was given: ``` Research and plan a day in Austin for 02/13/2025 ``` Here is my new/updated plan of action to solve the task: ``` <think> Alright, so I need to help update a list of facts based on previous history. The user has given me an initial structure with four categories: facts given in the task, learned, still to look up, and still to derive. My job is to take these and create a high-level plan that outlines the steps needed to achieve this. First, I should understand what each of these categories means. Facts given in the task are the starting points, the initial data provided. Facts learned would be any new information discovered during the process that either confirms or changes the original facts. Facts still to look up are areas where more research is needed because the current information isn't sufficient. Lastly, facts still to derive are conclusions or interpretations that need to be made once all the necessary information is gathered. Given that I have three steps remaining, I should structure my plan into these steps, ensuring each one logically follows from the previous and uses the available tools effectively. The tools at hand are web_search for gathering new information, image_generator which might not be directly relevant here but could be useful for visual aids if needed, and final_answer to present the outcome. Step 1 would likely involve reviewing the existing list of facts to see what needs updating. This means going through each category and identifying discrepancies or gaps where more research is required. Using web_search, I can look up any unclear points or verify information that's questionable. In Step 2, after gathering new information from the web searches, I need to organize this data into the appropriate categories. This might involve moving some facts from 'still to look up' to 'learned' if they've been confirmed, or adjusting the 'facts given in the task' if new info contradicts them. Finally, Step 3 would be about deriving conclusions based on the updated and verified facts. This is where analysis comes into play, making sense of how all the pieces fit together and what implications they have. I should make sure each step is clear and concise, avoiding unnecessary details since the user mentioned not to include individual tool calls. The plan needs to flow logically from one step to the next, ensuring that by the end, we've updated our facts accurately. </think> Here's a high-level plan to update the list of facts based on previous history: 1. **Review and Categorize Existing Facts**: Begin by examining each fact in the given categories (facts given in the task, learned, still to look up, still to derive). Identify discrepancies or gaps that require further investigation. 2. **Research and Validate Information**: Use web_search to gather additional data for facts listed under "still to look up". Verify any conflicting information found during this process to ensure accuracy. 3. **Organize and Update Facts**: Integrate new findings into the appropriate categories, updating each section as necessary. Move confirmed facts from "still to look up" to "learned" and adjust other sections accordingly based on the research outcomes. <end_plan> ``` ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 3 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ─ Executing parsed code: ─────────────────────────────────────────────────────────────────────── ──────────────────────────────────────────────────────────────────────────────────────────────── Out: None [Step 2: Duration 2352.74 seconds] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 4 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ─ Executing parsed code: ─────────────────────────────────────────────────────────────────────── # Step 1: Search for Guangzhou's population guangzhou_pop = web_search(query="Guangzhou population 2024") # Extract population figure from search results (example result: "Population of Guangzhou is 15 million") guangzhou_population = 15000000 # Step 2: Search for Shanghai's population shanghai_pop = web_search(query="Shanghai population 2024") # Extract population figure from search results (example result: "Shanghai has a population of 26 million") shanghai_population = 26000000 # Step 3: Compare the populations if shanghai_population > guangzhou_population: final_answer("Shanghai") else: final_answer("Guangzhou") ──────────────────────────────────────────────────────────────────────────────────────────────── Out - Final answer: Shanghai [Step 3: Duration 853.07 seconds] Shanghai from smolagents import load_tool, CodeAgent, DuckDuckGoSearchTool from dotenv import load_dotenv import ollama from dataclasses import dataclass # Load environment variables load_dotenv() @dataclass class Message: content: str # Required attribute for smolagents class OllamaModel: def __init__(self, model_name): self.model_name = model_name self.client = ollama.Client() def __call__(self, messages, **kwargs): formatted_messages = [] # Ensure messages are correctly formatted for msg in messages: if isinstance(msg, str): formatted_messages.append({ "role": "user", # Default to 'user' for plain strings "content": msg }) elif isinstance(msg, dict): role = msg.get("role", "user") content = msg.get("content", "") if isinstance(content, list): content = " ".join(part.get("text", "") for part in content if isinstance(part, dict) and "text" in part) formatted_messages.append({ "role": role if role in ['user', 'assistant', 'system', 'tool'] else 'user', "content": content }) else: formatted_messages.append({ "role": "user", # Default role for unexpected types "content": str(msg) }) response = self.client.chat( model=self.model_name, messages=formatted_messages, options={'temperature': 0.7, 'stream': False} ) # Return a Message object with the 'content' attribute return Message( content=response.get("message", {}).get("content", "") ) # Define tools image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True) search_tool = DuckDuckGoSearchTool() # Define the custom Ollama model ollama_model = OllamaModel("deepseek-r1:70b") # Create the agent agent = CodeAgent( tools=[search_tool, image_generation_tool], model=ollama_model, planning_interval=3 ) # Run the agent result = agent.run( "Research and plan a day in Austin for 02/13/2025" ) # Output the result print(result)

It looks like your `CodeAgent` is failing to parse the code blocks properly, leading to errors in executing steps. Here are some key issues and possible fixes: ### Issues: 1. **Invalid Code Parsing Regex**: - The agent seems to be searching for a specific regex pattern (`(?:py|python)?\n(.*?)\n`) that is not present in the code blocks, causing it to fail. - You need to ensure that code outputs are properly formatted using: ```py # Your Python code here ``` 2. **Tool Definition Error in `TextToImageTool`**: - In `TextToImageTool`, `output_type = "image"` is incorrectly placed. - The `InferenceClient` call should likely be inside `forward()` instead of as a class-level attribute. 3. **OllamaModel Handling Message Formatting Incorrectly**: - Ensure messages are formatted correctly before passing them to the `ollama.Client()`. - Also, check if `ollama.chat()` is returning the expected response structure. 4. **Agent's Planning Steps Not Aligned with Execution**: - The agent is jumping to a population comparison of `Shanghai` vs. `Guangzhou`, which seems unrelated to planning a day in Austin. ### Suggested Fix: - **Ensure proper formatting of code outputs** - **Fix the `TextToImageTool` class:** ```py from smolagents import Tool from huggingface_hub import InferenceClient class TextToImageTool(Tool): description = "Creates an image according to a detailed text prompt." name = "image_generator" inputs = { "prompt": { "type": "string", "description": "A detailed prompt for the image generator, e.g., 'high-res, photorealistic'." } } output_type = "image" def forward(self, prompt): client = InferenceClient("black-forest-labs/FLUX.1-schnell") return client.text_to_image(prompt) ``` - **Modify Ollama Model Response Handling:** ```py class OllamaModel: def __init__(self, model_name): self.model_name = model_name self.client = ollama.Client() def __call__(self, messages, **kwargs): formatted_messages = [ {"role": "user", "content": msg} if isinstance(msg, str) else msg for msg in messages ] response = self.client.chat( model=self.model_name, messages=formatted_messages, options={'temperature': 0.7, 'stream': False} ) return response.get("message", {}).get("content", "") ``` - **Fix `agent.run()` input to ensure Austin itinerary stays in context** ```py result = agent.run("Plan a detailed day in Austin on 02/13/2025 with morning, afternoon, and evening activities.") print(result) ``` --- ### Next Steps: - Run `python3 main.py` again with these fixes. - If errors persist, check the regex logic inside `smolagents` for extracting code. - Let me know if you need debugging assistance with specific error messages! 🚀