Delete app.py

app.py

@@ -1,96 +0,0 @@
-import os
-os.environ['TRANSFORMERS_CACHE'] = '/tmp/transformers'
-
-from fastapi import FastAPI, Header, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel, Field
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from typing import Optional, Dict, Annotated
-import logging
-import torch
-import os
-
-# Initialize logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Load model
-MODEL_NAME = "defog/sqlcoder-7b-2"
-logger.info(f"Loading model: {MODEL_NAME}")
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_NAME,
-    device_map="auto",
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
-)
-
-# FastAPI init
-app = FastAPI(title="Text to SQL API")
-
-# CORS for Hugging Face Space
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Request Model
-class RequestModel(BaseModel):
-    entity_urn: str
-    prompt: str
-
-# Response Model
-class ResponseModel(BaseModel):
-    message: str
-    result: str
-    action_type: str
-    entity_urn: str
-    metadata: Optional[Dict] = None
-
-@app.get("/")
-async def root():
-    return {
-        "message": "Text-to-SQL API running",
-        "docs": "/docs",
-        "health": "/health"
-    }
-
-@app.get("/health")
-async def health():
-    return {"status": "healthy"}
-
-@app.post("/generate", response_model=ResponseModel)
-async def generate_sql(
-    request: RequestModel,
-    x_api_key: Annotated[str, Header()]  # Optional token check
-):
-    try:
-        if not request.prompt.strip():
-            return ResponseModel(
-                message="failure",
-                result="Empty prompt",
-                action_type="text_to_sql",
-                entity_urn=request.entity_urn
-            )
-
-        inputs = tokenizer(request.prompt, return_tensors="pt").to(model.device)
-        outputs = model.generate(**inputs, max_length=512)
-        sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-        return ResponseModel(
-            message="success",
-            result=sql.strip(),
-            action_type="text_to_sql",
-            entity_urn=request.entity_urn,
-            metadata={"tokens": len(inputs["input_ids"][0])}
-        )
-
-    except Exception as e:
-        logger.error(f"Error: {str(e)}")
-        return ResponseModel(
-            message="failure",
-            result=f"Error: {str(e)}",
-            action_type="text_to_sql",
-            entity_urn=request.entity_urn
-        )