Rename main.py to app.py

main.py → app.py

@@ -6,15 +6,14 @@ from pydantic import BaseModel
 from sentence_transformers import SentenceTransformer, util
 from huggingface_hub import snapshot_download
 
-# --- Cache Configuration ---
+# --- Cache Config ---
 os.environ["HF_HOME"] = "/app/hf_cache"
 os.environ["TRANSFORMERS_CACHE"] = "/app/hf_cache"
 os.environ["TORCH_DISABLE_CUDA"] = "1"
 
-# --- Hugging Face Repo ---
+# --- Download Model & Embeddings from Hub ---
 HF_REPO = "Sp2503/Muril-Model"
 
-# --- Download model & embeddings from Hugging Face Hub ---
 print("📦 Downloading model & embeddings from Hugging Face Hub...")
 model_dir = snapshot_download(repo_id=HF_REPO, repo_type="model")
 print(f"✅ Model snapshot available at: {model_dir}")
@@ -23,14 +22,14 @@ MODEL_PATH = model_dir
 CSV_PATH = os.path.join(model_dir, "muril_multilingual_dataset.csv")
 EMBED_PATH = os.path.join(model_dir, "answer_embeddings.pt")
 
-# --- Load resources ---
+# --- Load Model ---
 print("⚙️ Loading model and embeddings...")
 model = SentenceTransformer(MODEL_PATH)
 df = pd.read_csv(CSV_PATH).dropna(subset=['question', 'answer'])
 answer_embeddings = torch.load(EMBED_PATH, map_location="cpu")
 print("✅ Model and embeddings loaded successfully.")
 
-# --- FastAPI Setup ---
+# --- FastAPI App ---
 app = FastAPI(title="MuRIL Multilingual QA API")
 
 class QueryRequest(BaseModel):
@@ -42,7 +41,7 @@ class QAResponse(BaseModel):
 
 @app.get("/")
 def root():
-    return {"status": "✅ API ready", "model_loaded": True}
+    return {"status": "✅ API is running", "model_loaded": True}
 
 @app.post("/get-answer", response_model=QAResponse)
 def get_answer_endpoint(request: QueryRequest):
@@ -64,7 +63,3 @@ def get_answer_endpoint(request: QueryRequest):
     best_idx = torch.argmax(cosine_scores).item()
     answer = filtered_df.iloc[best_idx]['answer']
     return {"answer": answer}
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run("main:app", host="0.0.0.0", port=8080)