strictly type python backend

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+  "python.analysis.typeCheckingMode": "strict"
+}

backend/image_generation.py

@@ -1,15 +1,15 @@
 import asyncio
-import os
 import re
+from typing import Dict, List, Union
 from openai import AsyncOpenAI
 from bs4 import BeautifulSoup
 
 
-async def process_tasks(prompts, api_key, base_url):
+async def process_tasks(prompts: List[str], api_key: str, base_url: str):
     tasks = [generate_image(prompt, api_key, base_url) for prompt in prompts]
     results = await asyncio.gather(*tasks, return_exceptions=True)
 
-    processed_results = []
+    processed_results: List[Union[str, None]] = []
     for result in results:
         if isinstance(result, Exception):
             print(f"An exception occurred: {result}")
@@ -20,9 +20,9 @@ async def process_tasks(prompts, api_key, base_url):
     return processed_results
 
 
-async def generate_image(prompt, api_key, base_url):
+async def generate_image(prompt: str, api_key: str, base_url: str):
     client = AsyncOpenAI(api_key=api_key, base_url=base_url)
-    image_params = {
+    image_params: Dict[str, Union[str, int]] = {
         "model": "dall-e-3",
         "quality": "standard",
         "style": "natural",
@@ -35,7 +35,7 @@ async def generate_image(prompt, api_key, base_url):
     return res.data[0].url
 
 
-def extract_dimensions(url):
+def extract_dimensions(url: str):
     # Regular expression to match numbers in the format '300x200'
     matches = re.findall(r"(\d+)x(\d+)", url)
 
@@ -48,11 +48,11 @@ def extract_dimensions(url):
         return (100, 100)
 
 
-def create_alt_url_mapping(code):
+def create_alt_url_mapping(code: str) -> Dict[str, str]:
     soup = BeautifulSoup(code, "html.parser")
     images = soup.find_all("img")
 
-    mapping = {}
+    mapping: Dict[str, str] = {}
 
     for image in images:
         if not image["src"].startswith("https://placehold.co"):
@@ -61,7 +61,9 @@ def create_alt_url_mapping(code):
     return mapping
 
 
-async def generate_images(code, api_key, base_url, image_cache):
+async def generate_images(
+    code: str, api_key: str, base_url: Union[str, None], image_cache: Dict[str, str]
+):
     # Find all images
     soup = BeautifulSoup(code, "html.parser")
     images = soup.find_all("img")

backend/llm.py

@@ -1,16 +1,16 @@
-import os
-from typing import Awaitable, Callable
+from typing import Awaitable, Callable, List
 from openai import AsyncOpenAI
+from openai.types.chat import ChatCompletionMessageParam, ChatCompletionChunk
 
 MODEL_GPT_4_VISION = "gpt-4-vision-preview"
 
 
 async def stream_openai_response(
-    messages,
+    messages: List[ChatCompletionMessageParam],
     api_key: str,
     base_url: str | None,
     callback: Callable[[str], Awaitable[None]],
-):
+) -> str:
     client = AsyncOpenAI(api_key=api_key, base_url=base_url)
 
     model = MODEL_GPT_4_VISION
@@ -23,9 +23,10 @@ async def stream_openai_response(
         params["max_tokens"] = 4096
         params["temperature"] = 0
 
-    completion = await client.chat.completions.create(**params)
+    stream = await client.chat.completions.create(**params)  # type: ignore
     full_response = ""
-    async for chunk in completion:
+    async for chunk in stream:  # type: ignore
+        assert isinstance(chunk, ChatCompletionChunk)
         content = chunk.choices[0].delta.content or ""
         full_response += content
         await callback(content)

backend/main.py

@@ -14,8 +14,10 @@
 from fastapi.responses import HTMLResponse
 import openai
 from llm import stream_openai_response
-from mock import mock_completion
+from openai.types.chat import ChatCompletionMessageParam
+from mock_llm import mock_completion
 from utils import pprint_prompt
+from typing import Dict, List
 from image_generation import create_alt_url_mapping, generate_images
 from prompts import assemble_prompt
 from routes import screenshot
@@ -53,7 +55,7 @@ async def get_status():
     )
 
 
-def write_logs(prompt_messages, completion):
+def write_logs(prompt_messages: List[ChatCompletionMessageParam], completion: str):
     # Get the logs path from environment, default to the current working directory
     logs_path = os.environ.get("LOGS_PATH", os.getcwd())
 
@@ -84,7 +86,8 @@ async def throw_error(
         await websocket.send_json({"type": "error", "value": message})
         await websocket.close()
 
-    params = await websocket.receive_json()
+    # TODO: Are the values always strings?
+    params: Dict[str, str] = await websocket.receive_json()
 
     print("Received params")
 
@@ -154,7 +157,7 @@ async def throw_error(
     print("generating code...")
     await websocket.send_json({"type": "status", "value": "Generating code..."})
 
-    async def process_chunk(content):
+    async def process_chunk(content: str):
         await websocket.send_json({"type": "chunk", "value": content})
 
     # Assemble the prompt
@@ -176,15 +179,23 @@ async def process_chunk(content):
         return
 
     # Image cache for updates so that we don't have to regenerate images
-    image_cache = {}
+    image_cache: Dict[str, str] = {}
 
     if params["generationType"] == "update":
         # Transform into message format
         # TODO: Move this to frontend
         for index, text in enumerate(params["history"]):
-            prompt_messages += [
-                {"role": "assistant" if index % 2 == 0 else "user", "content": text}
-            ]
+            if index % 2 == 0:
+                message: ChatCompletionMessageParam = {
+                    "role": "assistant",
+                    "content": text,
+                }
+            else:
+                message: ChatCompletionMessageParam = {
+                    "role": "user",
+                    "content": text,
+                }
+            prompt_messages.append(message)
         image_cache = create_alt_url_mapping(params["history"][-2])
 
     if SHOULD_MOCK_AI_RESPONSE:

backend/mock.py → backend/mock_llm.py

@@ -1,7 +1,8 @@
 import asyncio
+from typing import Awaitable, Callable
 
 
-async def mock_completion(process_chunk):
+async def mock_completion(process_chunk: Callable[[str], Awaitable[None]]) -> str:
     code_to_return = NO_IMAGES_NYTIMES_MOCK_CODE
 
     for i in range(0, len(code_to_return), 10):

backend/prompts.py

@@ -1,3 +1,8 @@
+from typing import List, Union
+
+from openai.types.chat import ChatCompletionMessageParam, ChatCompletionContentPartParam
+
+
 TAILWIND_SYSTEM_PROMPT = """
 You are an expert Tailwind developer
 You take screenshots of a reference web page from the user, and then build single page apps 
@@ -117,8 +122,10 @@
 
 
 def assemble_prompt(
-    image_data_url, generated_code_config: str, result_image_data_url=None
-):
+    image_data_url: str,
+    generated_code_config: str,
+    result_image_data_url: Union[str, None] = None,
+) -> List[ChatCompletionMessageParam]:
     # Set the system prompt based on the output settings
     system_content = TAILWIND_SYSTEM_PROMPT
     if generated_code_config == "html_tailwind":
@@ -132,7 +139,7 @@ def assemble_prompt(
     else:
         raise Exception("Code config is not one of available options")
 
-    user_content = [
+    user_content: List[ChatCompletionContentPartParam] = [
         {
             "type": "image_url",
             "image_url": {"url": image_data_url, "detail": "high"},

backend/routes/screenshot.py

@@ -11,7 +11,9 @@ def bytes_to_data_url(image_bytes: bytes, mime_type: str) -> str:
     return f"data:{mime_type};base64,{base64_image}"
 
 
-async def capture_screenshot(target_url, api_key, device="desktop") -> bytes:
+async def capture_screenshot(
+    target_url: str, api_key: str, device: str = "desktop"
+) -> bytes:
     api_base_url = "https://api.screenshotone.com/take"
 
     params = {

backend/utils.py

@@ -1,28 +1,30 @@
 import copy
 import json
+from typing import List
+from openai.types.chat import ChatCompletionMessageParam
 
 
-def pprint_prompt(prompt_messages):
+def pprint_prompt(prompt_messages: List[ChatCompletionMessageParam]):
     print(json.dumps(truncate_data_strings(prompt_messages), indent=4))
 
 
-def truncate_data_strings(data):
+def truncate_data_strings(data: List[ChatCompletionMessageParam]):  # type: ignore
     # Deep clone the data to avoid modifying the original object
     cloned_data = copy.deepcopy(data)
 
     if isinstance(cloned_data, dict):
-        for key, value in cloned_data.items():
+        for key, value in cloned_data.items():  # type: ignore
             # Recursively call the function if the value is a dictionary or a list
             if isinstance(value, (dict, list)):
-                cloned_data[key] = truncate_data_strings(value)
+                cloned_data[key] = truncate_data_strings(value)  # type: ignore
             # Truncate the string if it it's long and add ellipsis and length
             elif isinstance(value, str):
-                cloned_data[key] = value[:40]
+                cloned_data[key] = value[:40]  # type: ignore
                 if len(value) > 40:
-                    cloned_data[key] += "..." + f" ({len(value)} chars)"
+                    cloned_data[key] += "..." + f" ({len(value)} chars)"  # type: ignore
 
-    elif isinstance(cloned_data, list):
+    elif isinstance(cloned_data, list):  # type: ignore
         # Process each item in the list
-        cloned_data = [truncate_data_strings(item) for item in cloned_data]
+        cloned_data = [truncate_data_strings(item) for item in cloned_data]  # type: ignore
 
-    return cloned_data
+    return cloned_data  # type: ignore