Add Kaggle Dataset Visualization With LLM-Generated Code Using Ollama And Judge0

Author: hermanzdosilovic

README.md

@@ -233,7 +233,7 @@ print(client.get_languages())
 
 ### Running LLM-Generated Code
 
-#### Simple Example with Ollama
+#### Simple Example With Ollama
 
 ```python
 # pip install judge0 ollama
@@ -270,7 +270,7 @@ result = judge0.run(source_code=code, language=judge0.C)
 print(f"CODE EXECUTION RESULT:\n{result.stdout}")
 ```
 
-#### Tool Calling (a.k.a. Function Calling) with Ollama
+#### Tool Calling (a.k.a. Function Calling) With Ollama
 
 ```python
 # pip install judge0 ollama
@@ -416,6 +416,108 @@ result = code_runner.initiate_chat(
 print(f"Result: {result.summary}")
 ```
 
+#### Kaggle Dataset Visualization With LLM-Generated Code Using Ollama And Judge0
+
+```python
+# pip install judge0 ollama requests
+import os
+import zipfile
+
+import judge0
+import requests
+from judge0 import File, Filesystem
+from ollama import Client
+
+# Step 1: Download the dataset from Kaggle.
+dataset_url = "https://www.kaggle.com/api/v1/datasets/download/gregorut/videogamesales"
+dataset_zip_path = "vgsales.zip"
+dataset_csv_path = "vgsales.csv"  # P.S.: We know the CSV file name inside the zip.
+
+if not os.path.exists(dataset_csv_path):  # Download only if not already downloaded.
+    with requests.get(dataset_url) as response:
+        with open(dataset_zip_path, "wb") as f:
+            f.write(response.content)
+            with zipfile.ZipFile(dataset_zip_path, "r") as f:
+                f.extractall(".")
+
+# Step 2: Prepare the submission for Judge0.
+with open(dataset_csv_path, "r") as f:
+    submission = judge0.Submission(
+        language=judge0.PYTHON_FOR_ML,
+        additional_files=Filesystem(
+            content=[
+                File(name=dataset_csv_path, content=f.read()),
+            ]
+        ),
+    )
+
+# Step 3: Initialize Ollama Client. Get your free tier Ollama Cloud API key at https://ollama.com.
+client = Client(
+    host="https://ollama.com",
+    headers={"Authorization": "Bearer " + os.environ.get("OLLAMA_API_KEY")},
+)
+
+# Step 4: Prepare the prompt, messages, tools, and choose the model.
+prompt = f"""
+I have a CSV that contains a list of video games with sales greater than 100,000 copies. It's saved in the file {dataset_csv_path}.
+These are the columns:
+- 'Rank': Ranking of overall sales
+- 'Name': The games name
+- 'Platform': Platform of the games release (i.e. PC,PS4, etc.)
+- 'Year': Year of the game's release
+- 'Genre': Genre of the game
+- 'Publisher': Publisher of the game
+- 'NA_Sales': Sales in North America (in millions)
+- 'EU_Sales': Sales in Europe (in millions)
+- 'JP_Sales': Sales in Japan (in millions)
+- 'Other_Sales': Sales in the rest of the world (in millions)
+- 'Global_Sales': Total worldwide sales.
+
+I want to better understand how the sales are distributed across different genres over the years.
+Write Python code that analyzes the dataset based on my request, produces right chart and saves it as an image file.
+"""
+messages = [{"role": "user", "content": prompt}]
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "execute_python",
+            "description": "Execute the Python programming language code.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "code": {
+                        "type": "string",
+                        "description": "The code written in the Python programming language.",
+                    }
+                },
+                "required": ["code"],
+            },
+        },
+    }
+]
+model = "qwen3-coder:480b-cloud"
+
+# Step 5: Start the interaction with the model.
+response = client.chat(model=model, messages=messages, tools=tools)
+response_message = response["message"]
+
+if response_message.tool_calls:
+    for tool_call in response_message.tool_calls:
+        if tool_call.function.name == "execute_python":
+            code = tool_call.function.arguments["code"]
+            print(f"CODE GENERATED BY THE MODEL:\n{code}\n")
+
+            submission.source_code = code
+            result = judge0.run(submissions=submission)
+
+            for f in result.post_execution_filesystem:
+                if f.name.endswith((".png", ".jpg", ".jpeg")):
+                    with open(f.name, "wb") as img_file:
+                        img_file.write(f.content)
+                    print(f"Generated image saved as: {f.name}\n")
+```
+
 ### Filesystem
 
 This example shows how to use Judge0 Python SDK to: