test

README.md

@@ -6,6 +6,7 @@ colorTo: green
 sdk: docker
 pinned: false
 license: apache-2.0
+app_file: run.py
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

@@ -1,54 +1,28 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from typing import List
-from src.search_engine import PromptSearchEngine
-from src.prompt_loader import PromptLoader
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
 
-# Constants
-SEED = 42
-DATA_SIZE = 100
+app = FastAPI(title="Deploying FastAPI Apps on Huggingface")
 
-# Initialize the prompt loader and search engine
-prompts = PromptLoader(seed=SEED).load_data(size=DATA_SIZE)
-engine = PromptSearchEngine(prompts)
 
-# Initialize FastAPI
-app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
 
-# Request and Response Models
-class QueryRequest(BaseModel):
-    query: str
-    n: int = 5
+@app.get("/", tags=["Home"])
+def api_home():
+    return {"detail": "Welcome to FastAPI TextGen Tutorial!"}
 
 
-class SimilarPrompt(BaseModel):
-    score: float
-    prompt: str
-
-
-class QueryResponse(BaseModel):
-    similar_prompts: List[SimilarPrompt]
-
-
-@app.get("/")
-def read_root():
-    return {"message": "Hello, FastAPI!"}
-
-
-# API endpoint
-@app.post("/most_similar", response_model=QueryResponse)
-async def get_most_similar(query_request: QueryRequest):
-    try:
-        similar_prompts = engine.most_similar(
-            query=query_request.query, n=query_request.n
-        )
-        response = QueryResponse(
-            similar_prompts=[
-                SimilarPrompt(score=score, prompt=prompt)
-                for score, prompt in similar_prompts
-            ]
-        )
-        return response
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+@app.post(
+    "/api/generate",
+    summary="Generate text from prompt",
+    tags=["Generate"],
+    response_model=Generate,
+)
+def inference(input_prompt: str):
+    return "Hello"