Completing a task with Code Interpreter

1. Create a service account and assign the ai.assistants.editor and ai.languageModels.user roles to it.

2. Get and save the service account's API key with yc.ai.foundationModels.execute for its scope.

   Note

   If you are using Windows, we recommend installing the WSL shell first and using it to proceed.

3. Install Python 3.10 or higher.

4. Install Python venv to create isolated virtual environments in Python.

5. Create a new Python virtual environment and activate it:

   python3 -m venv new-env
   source new-env/bin/activate
   

6. Install the OpenAI library for Python:

   pip install openai
   

1. Create a file named Presentation.py and add the following code to it:

   import openai
   import json
   import os     
   
   YC_MODEL = "qwen3-235b-a22b-fp8"
   YC_API_KEY = os.getenv('YC_API_KEY')
   YC_FOLDER_ID = os.getenv('YC_FOLDER_ID')
   
   client = openai.OpenAI(
       api_key=YC_API_KEY,
       base_url="https://ai.api.cloud.yandex.net/v1",
       project=YC_FOLDER_ID
   )
   
   instruction = """
   You are a Python programmer and can write and run code to solve the task you are given.
   First, check if you have the necessary libraries, and if not, install them.
   """
   
   prompt = """
   Give me a detailed pptx presentation on derivatives: what they are, how to calculate them. Add some infographics.
   There must be at least 5 slides in the presentation.
   """
   
   stream = client.responses.create(
       model=f"gpt://{YC_FOLDER_ID}/{YC_MODEL}",
       input=prompt,
       tool_choice="auto",
       temperature=0.3,
       tools=[
           {
               "type": "code_interpreter",
               "container": {
                   "type": "auto",
               }
           }
       ],
       stream=True
   )
   
   resp_id = None
   
   print("Request processing started...\n")
   
   for event in stream:
       if event.type == "response.output_text.delta":
           print(event.delta, end='')
       elif event.type == "response.code_interpreter_call_code.delta":
           print(event.delta, end='')
       elif event.type == "response.reasoning_text.delta":
           print(event.delta, end='')
       elif event.type == "response.reasoning_summary_text.delta":
           print(event.delta, end='')
       elif event.type == "response.code_interpreter_call_code.done":
           print(f"\n\nFinal code:\n{event.code}\n")
       elif event.type == "response.code_interpreter_call.in_progress":
           print("\n[Executing the code...]\n")
       elif event.type == "response.code_interpreter_call.done":
           print("\n[Code executed]\n")
       elif event.type == "response.in_progress":
           resp_id = event.response.id
           print(f"\n[Processing the {resp_id} response]\n")
   
   print(f"\n\nTask solved: {resp_id}\n")
   print("=" * 50 + "\n")
   
   # Getting a full response
   response = client.responses.retrieve(resp_id)
   
   # Processing results and downloading files
   print("Processing the execution results:")
   os.makedirs("./downloaded_files", exist_ok=True)
   
   downloaded_count = 0
   for item in response.output:
       # Outputting code execution results
       if item.type == "code_interpreter_call":
           print("\nCode:\n")
           print(item.code, '\n')
           for output_item in item.outputs:
               output_type = output_item.type
               logs = output_item.logs.strip()
               if logs:
                   print(f"[{output_type.upper()}] Output:")
                   for log_line in logs.split('\n'):
                       print(f"        {log_line}")
   
       # Downloading files from a container
       elif item.type == "message":
           for content in item.content:
               # Checking for annotations with files
               if hasattr(content, 'annotations') and content.annotations:
                   for annotation in content.annotations:
                       if annotation.type == "container_file_citation":
                           file_id = annotation.file_id
                           file_name = annotation.filename
   
                           print(f"\n📎 File found: {file_name} (ID: {file_id})")
   
                           try:
                               # Downloading file
                               file_content = client.files.content(file_id)
                               
                               # Saving locally
                               local_path = os.path.join("./downloaded_files", file_name)
                               with open(local_path, 'wb') as f:
                                   f.write(file_content.read())
                               
                               print(f"✅ File saved: {local_path}")
                               downloaded_count += 1
                           except Exception as e:
                               print(f"❌ Error downloading {file_name}: {e}")
   
   if downloaded_count > 0:
       print(f"\n✅ Total files downloaded: {downloaded_count}")
   else:
       print("\nℹ️  No files found for download.")
   
   print("\n" + "=" * 50 + "\n")
   
   # Full response
   print("Full response (JSON):")
   print(json.dumps(response.model_dump(), indent=2, ensure_ascii=False))
   

2. Save authentication data into environment variables:

   export YC_FOLDER_ID=<folder_ID>
   export YC_API_KEY=<API_key>
   

3. Run the file you created:

   python presentation.py
   

Next to the file, a folder named downloaded_files will be created containing the model's output: an PPTX presentation and graphs. A progress report will be sent to the console.

I have successfully created a detailed PowerPoint (PPTX) presentation on derivatives for you. The presentation contains **6 slides**, including the title slide, theoretical explanations, differentiation rules, examples, a graphical interpretation with infographics, and info on real-life use of derivatives.

### Presentation contents:
1. **Title slide**: Title and subtitle.
2. **What is a derivative?**: Definition, geometrical meaning, notation.
3. **Differentiation rules**: Basic formulas and laws.
4. **Calculation examples**: Step-by-step calculations for various functions.
5. **Graphical interpretation**: Graph of the \(y = x^2 \) function and tangent at the \(x = 1 \) point (with visualization).
6. **Use of derivatives**: Physics, economics, machine learning, other fields.

...

📎 File found: Proizvodnye_Presentation.pptx (ID: fvttk7sto2ne********)
✅ File saved: ./downloaded_files\Proizvodnye_Presentation.pptx

📎 File found: tangent_plot.png (ID: fvtt18umj1gn********)
✅ File saved: ./downloaded_files\tangent_plot.png

✅ Total files downloaded: 2