chore: import upstream snapshot with attribution

examples/codegen-from-schema/create_fastapi_app.py

@@ -0,0 +1,121 @@
+import json
+import datetime
+from pathlib import Path
+from jinja2 import Template
+import re
+from datamodel_code_generator import InputFileType, generate
+from pydantic import BaseModel
+
+APP_TEMPLATE_STR = '''# generated by instructor-codegen:
+#   timestamp: {{timestamp}}
+#   task_name: {{task_name}}
+#   api_path: {{api_path}}
+#   json_schema_path: {{json_schema_path}}
+
+from fastapi import FastAPI
+from pydantic import BaseModel
+from jinja2 import Template
+from models import {{title}}
+
+import openai
+import instructor
+
+instructor.from_openai()
+
+app = FastAPI()
+
+class TemplateVariables(BaseModel):
+{% for var in jinja_vars %}
+    {{var.strip()}}: str
+{% endfor %}
+
+class RequestSchema(BaseModel):
+    template_variables: TemplateVariables
+    model: str
+    temperature: int
+
+PROMPT_TEMPLATE = Template("""{{prompt_template}}""".strip())
+
+@app.post("{{api_path}}", response_model={{title}})
+async def {{task_name}}(input: RequestSchema) -> {{title}}:
+    rendered_prompt = PROMPT_TEMPLATE.render(**input.template_variables.model_dump())
+    return await openai.ChatCompletion.acreate(
+        model=input.model,
+        temperature=input.temperature,
+        response_model={{title}},
+        messages=[
+            {"role": "user", "content": rendered_prompt}
+        ]
+    ) # type: ignore
+'''
+
+
+class TemplateVariables(BaseModel):
+    biography: str
+
+
+def load_json_schema(json_schema_path: str) -> dict:
+    try:
+        with open(json_schema_path) as f:
+            return json.load(f)
+    except Exception as e:
+        raise ValueError(f"Failed to load JSON schema: {e}") from e
+
+
+def generate_pydantic_model(json_schema_path: str):
+    input_path = Path(json_schema_path)
+    output_path = Path("./models.py")
+    generate(
+        input_=input_path, input_file_type=InputFileType.JsonSchema, output=output_path
+    )
+
+
+def extract_jinja_vars(prompt_template: str) -> list:
+    return re.findall(r"\{\{(.*?)\}\}", prompt_template)
+
+
+def render_app_template(template_str: str, **kwargs) -> str:
+    app_template = Template(template_str)
+    return app_template.render(**kwargs)
+
+
+def create_app(
+    api_path: str, task_name: str, json_schema_path: str, prompt_template: str
+) -> str:
+    if not api_path.startswith("/"):
+        api_path = "/" + api_path
+
+    schema = load_json_schema(json_schema_path)
+    title = schema["title"]
+    generate_pydantic_model(json_schema_path)
+
+    jinja_vars = extract_jinja_vars(prompt_template)
+
+    return render_app_template(
+        APP_TEMPLATE_STR,
+        timestamp=datetime.datetime.now().isoformat(),
+        task_name=task_name,
+        api_path=api_path,
+        json_schema_path=json_schema_path,
+        title=title,
+        jinja_vars=jinja_vars,
+        prompt_template=prompt_template,
+    )
+
+
+if __name__ == "__main__":
+    try:
+        fastapi_code = create_app(
+            api_path="/api/v1/extract_person",
+            task_name="extract_person",
+            json_schema_path="./input.json",
+            prompt_template="Extract the person from the following: {{biography}}",
+        )
+
+        with open("./run.py", "w") as f:
+            f.write(fastapi_code)
+
+        print("FastAPI application generated and saved to './run.py'")
+
+    except Exception as e:
+        print(f"An error occurred: {e}")