Upload app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Initialize FastAPI app
+app = FastAPI(title="Stress Detection API", version="1.0")
+
+model = AutoModelForSequenceClassification.from_pretrained("SrivarshiniGanesan/finetuned-stress-model")
+tokenizer = AutoTokenizer.from_pretrained("SrivarshiniGanesan/finetuned-stress-model")
+
+# Request format
+class TextInput(BaseModel):
+    text: str
+
+@app.post("/predict")
+def predict_stress(input_text: TextInput):
+    inputs = tokenizer(input_text.text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        prediction = torch.argmax(logits, dim=-1).item()
+    
+    return {"text": input_text.text, "stress_prediction": "Stress" if prediction == 1 else "No Stress"}
+
+@app.get("/")
+def home():
+    return {"message": "Welcome to the Stress Detection API!"}