add a gpt-engineer exmaple

examples/gpt-engineer/multi_file_program.py

@@ -0,0 +1,130 @@
+import openai
+
+from typing import List
+from pydantic import Field
+from openai_function_call import OpenAISchema
+
+
+class File(OpenAISchema):
+    """
+    Correctly named file with contents.
+    """
+
+    file_name: str = Field(
+        ..., description="The name of the file including the extension"
+    )
+    body: str = Field(..., description="Correct contents of a file")
+
+    def save(self):
+        with open(self.file_name, "w") as f:
+            f.write(self.body)
+
+
+class Program(OpenAISchema):
+    """
+    Set of files that represent a complete and correct program
+    """
+
+    files: List[File] = Field(..., description="List of files")
+
+
+def segment(data: str) -> Program:
+    completion = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-0613",
+        temperature=0.1,
+        functions=[Program.openai_schema],
+        function_call={"name": Program.openai_schema["name"]},
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a world class programming AI capable of writing correct python scripts and modules. You will name files correct, include __init__.py files and write correct python code. with correct imports.",
+            },
+            {
+                "role": "user",
+                "content": data,
+            },
+        ],
+        max_tokens=1000,
+    )
+    return Program.from_response(completion)
+
+
+if __name__ == "__main__":
+    queries = segment(
+        """
+        Create a fastapi app with a readme.md file and a main.py file with 
+        some basic math functions. the datamodels should use pydantic and 
+        the main.py should use fastapi. the readme.md should have a title 
+        and a description. The readme should contain some helpful infromation 
+        and a curl example"""
+    )
+
+    for file in queries.files:
+        print(file.file_name)
+        print("-")
+        print(file.body)
+        print("\n\n\n")
+    """
+    readme.md
+    -
+    # FastAPI App
+
+    This is a FastAPI app that provides some basic math functions.
+
+    ## Usage
+
+    To use this app, follow the instructions below:
+
+    1. Install the required dependencies by running `pip install -r requirements.txt`.
+    2. Start the app by running `uvicorn main:app --reload`.
+    3. Open your browser and navigate to `http://localhost:8000/docs` to access the Swagger UI documentation.
+
+    ## Example
+
+    You can use the following curl command to test the `/add` endpoint:
+
+    ```bash
+    $ curl -X POST -H "Content-Type: application/json" -d '{"a": 2, "b": 3}' http://localhost:8000/add
+    ```
+
+    main.py
+    -
+    from fastapi import FastAPI
+    from pydantic import BaseModel
+
+    app = FastAPI()
+
+
+    class Numbers(BaseModel):
+        a: int
+        b: int
+
+
+    @app.post('/add')
+    def add_numbers(numbers: Numbers):
+        return {'result': numbers.a + numbers.b}
+
+
+    @app.post('/subtract')
+    def subtract_numbers(numbers: Numbers):
+        return {'result': numbers.a - numbers.b}
+
+
+    @app.post('/multiply')
+    def multiply_numbers(numbers: Numbers):
+        return {'result': numbers.a * numbers.b}
+
+
+    @app.post('/divide')
+    def divide_numbers(numbers: Numbers):
+        if numbers.b == 0:
+            return {'error': 'Cannot divide by zero'}
+        return {'result': numbers.a / numbers.b}
+
+
+    requirements.txt
+    -
+    fastapi
+    uvicorn
+    pydantic
+    """