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File size: 1,478 Bytes
9725757 65f5fc6 9725757 d233d25 65f5fc6 9725757 65f5fc6 9725757 65f5fc6 d233d25 9725757 f3e1db8 d233d25 9725757 65f5fc6 d233d25 65f5fc6 f3e1db8 65f5fc6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | from fastapi import FastAPI
from pydantic import BaseModel
from typing import Optional
from fastapi.middleware.cors import CORSMiddleware
from model_service import correct_code_with_ai, correct_code_with_gemini, get_gemini_models
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Allows all origins for simplicity.
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class CodeSnippet(BaseModel):
code: str
model_provider: str = "local" # "local" or "google"
api_key: Optional[str] = None
google_model_name: Optional[str] = None
class ModelRequest(BaseModel):
api_key: Optional[str] = None
@app.get("/api/health")
def health_check():
return {"status": "ok"}
@app.post("/api/models")
def list_models_endpoint(req: ModelRequest):
"""
Fetches available Google models using the provided (or env) API key.
"""
models = get_gemini_models(req.api_key)
return {"models": models}
@app.post("/api/correct")
def correct_code_endpoint(snippet: CodeSnippet):
if snippet.model_provider == "google":
result = correct_code_with_gemini(snippet.code, snippet.api_key, snippet.google_model_name)
else:
# Default to local
result = correct_code_with_ai(snippet.code)
# result is now a dict: {"code": "...", "language": {"name": "...", "ext": "..."}}
return {
"corrected_code": result["code"],
"language": result["language"]
} |