Update app.py

app.py

@@ -2,29 +2,36 @@ from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel, Field
 from llama_cpp import Llama
+from contextlib import asynccontextmanager
 import re
 
-# ==================== MODEL CONFIG ====================
 MODEL_REPO = "bartowski/Phi-3.5-mini-instruct-GGUF"
 MODEL_FILE = "Phi-3.5-mini-instruct-Q4_K_M.gguf"
 
-print("🚀 Loading Phi-3.5 Mini (Fast Summarizer)...")
-llm = Llama.from_pretrained(
-    repo_id=MODEL_REPO,
-    filename=MODEL_FILE,
-    n_threads=4,
-    n_ctx=2048,      # Enough for summarization
-    n_batch=256,
-    n_gpu_layers=0,
-    verbose=False,
-)
-print("✅ Model loaded")
+llm = None  # global reference
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global llm
+    print("🚀 Loading Phi-3.5 Mini (Fast Summarizer)...")
+    llm = Llama.from_pretrained(
+        repo_id=MODEL_REPO,
+        filename=MODEL_FILE,
+        n_threads=4,
+        n_ctx=2048,
+        n_batch=256,
+        n_gpu_layers=0,
+        verbose=False,
+    )
+    print("✅ Model loaded")
+    yield
+    print("🛑 Shutting down...")
 
-# ==================== FASTAPI ====================
 app = FastAPI(
     title="AI Summarizer",
     description="Fast & Accurate AI Text Summarizer",
-    version="1.0"
+    version="1.0",
+    lifespan=lifespan
 )
 
 app.add_middleware(
@@ -34,70 +41,54 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# ==================== REQUEST ====================
 class SummarizeRequest(BaseModel):
     text: str = Field(..., min_length=1, max_length=2000)
-    length: str = "short"  # short | medium | long
+    length: str = "short"
 
-# ==================== PROMPTS ====================
 LENGTH_INSTRUCTIONS = {
     "short": "Summarize in 2–3 concise sentences.",
     "medium": "Summarize in 4–5 clear sentences.",
     "long": "Summarize in a detailed paragraph.",
 }
 
-# ==================== CLEAN OUTPUT ====================
 def clean_output(text: str) -> str:
     text = re.sub(r"<\|.*?\|>", "", text)
     text = re.sub(r"\s+", " ", text)
     return text.strip()
 
-# ==================== ENDPOINT ====================
 @app.post("/api/summarize")
 async def summarize(req: SummarizeRequest):
-    text = req.text.strip()
-    if not text:
-        raise HTTPException(status_code=400, detail="Text cannot be empty")
+    if llm is None:
+        raise HTTPException(status_code=503, detail="Model not loaded")
 
+    text = req.text.strip()
     length_instruction = LENGTH_INSTRUCTIONS.get(req.length, LENGTH_INSTRUCTIONS["short"])
 
-    prompt = (
-        f"<|user|>\n"
-        f"You are an expert text summarizer.\n"
-        f"{length_instruction}\n\n"
-        f"Text:\n{text}\n"
-        f"<|end|>\n"
-        f"<|assistant|>\n"
+    prompt = f"""
+<|user|>
+You are an expert text summarizer.
+{length_instruction}
+
+Text:
+{text}
+<|end|>
+<|assistant|>
+"""
+
+    output = llm(
+        prompt,
+        max_tokens=140 if req.length == "short" else 220,
+        temperature=0.3,
+        top_p=0.9,
+        top_k=40,
+        repeat_penalty=1.05,
+        stop=["<|end|>", "<|user|>"],
     )
 
-    try:
-        output = llm(
-            prompt,
-            max_tokens=140 if req.length == "short" else 220,
-            temperature=0.3,     # Low = factual
-            top_p=0.9,
-            top_k=40,
-            repeat_penalty=1.05,
-            stop=["<|end|>", "<|user|>"],
-            echo=False,
-        )
-
-        summary = clean_output(output["choices"][0]["text"])
-
-        if not summary:
-            raise RuntimeError("Empty summary")
-
-        return {
-            "original_length": len(text),
-            "summary": summary,
-            "length": req.length,
-            "success": True
-        }
-
-    except Exception as e:
-        print("❌ Summarization error:", e)
-        raise HTTPException(status_code=500, detail="Summarization failed")
+    summary = clean_output(output["choices"][0]["text"])
+    return {"summary": summary, "success": True}
 
 @app.get("/")
 def health():
     return {"status": "ok", "model": MODEL_FILE}
+