Update app.py

app.py

@@ -1,8 +1,14 @@
+import os
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from fastapi import FastAPI
 from pydantic import BaseModel
 import torch
 
+# ✅ Fix Hugging Face cache permissions
+os.environ["HF_HOME"] = "/app/hf_cache"
+os.environ["TRANSFORMERS_CACHE"] = "/app/hf_cache"
+os.makedirs("/app/hf_cache", exist_ok=True)
+
 # Initialize FastAPI
 app = FastAPI(title="Check-ins Classifier API", version="1.0")
 
@@ -12,7 +18,7 @@ tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
 model.eval()
 
-# Define label mapping
+# Label mapping
 id2label = {
     0: "Bad",
     1: "Mediocre",
@@ -25,16 +31,12 @@ class InputText(BaseModel):
 
 @app.post("/predict")
 async def predict(data: InputText):
-    # Tokenize input
     inputs = tokenizer(data.text, return_tensors="pt", truncation=True, padding=True)
-    
-    # Model inference
     with torch.no_grad():
         outputs = model(**inputs)
         probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
         predicted_label_id = torch.argmax(probs, dim=-1).item()
-    
-    # Return JSON response
+
     return {
         "input_text": data.text,
         "predicted_label": id2label[predicted_label_id],
@@ -45,3 +47,4 @@ async def predict(data: InputText):
 @app.get("/")
 async def home():
     return {"message": "Welcome to the Check-ins Classifier API. Use POST /predict to classify text."}
+