fixingdgin

app.py

@@ -1,19 +1,27 @@
 import os
-# Use HF_HOME instead of TRANSFORMERS_CACHE, and point it to /tmp
-os.environ["HF_HOME"] = "/tmp"
+os.environ["HF_HOME"] = "/tmp"  # ensure Hugging Face cache is writable
 
 from fastapi import FastAPI
 from pydantic import BaseModel
 from transformers import pipeline
 
+# Load the Hugging Face model
 classifier = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-sms-spam-detection")
 
+# Define FastAPI app
+app = FastAPI()
+
+# Health check route
+@app.get("/")
+def read_root():
+    return {"status": "ok", "message": "API is running"}
+
+# Define request schema
 class Query(BaseModel):
     text: str
 
-app = FastAPI()
-
-@app.post("/predict")
+# Prediction route (POST /)
+@app.post("/")
 def predict(query: Query):
     result = classifier(query.text)[0]
     return {"label": result["label"], "score": result["score"]}

test.py

@@ -1,9 +1,17 @@
 import requests
 
+# Hugging Face Space base URL
+BASE_URL = "https://spam-fastapi-cjell.hf.space"
+
+# First check if API is live
+resp = requests.get(BASE_URL)
+print("Health check:", resp.status_code, resp.json())
+
+# Now send prediction request
 resp = requests.post(
-    "https://spam-fastapi-cjell.hf.space/predict",
+    BASE_URL,  # POST / (root) handles predictions
     json={"text": "Congratulations! You've won a free cruise!"}
 )
 
-print("Status:", resp.status_code)
-print("Raw text:", resp.text)
+print("Prediction status:", resp.status_code)
+print("Prediction response:", resp.json())