Create app.py

app.py

@@ -0,0 +1,62 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+app = FastAPI()
+
+# ----------------------------------
+# Load Model from Hugging Face Hub
+# ----------------------------------
+MODEL_NAME = "sm89/Symptom2Disease"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+model.eval()
+
+# ----------------------------------
+# Request Schema
+# ----------------------------------
+class PredictionRequest(BaseModel):
+    text: str
+
+# ----------------------------------
+# Health Check
+# ----------------------------------
+@app.get("/")
+def home():
+    return {"message": "Medical Symptom API Running"}
+
+# ----------------------------------
+# Prediction Endpoint
+# ----------------------------------
+@app.post("/predict")
+def predict(request: PredictionRequest):
+
+    inputs = tokenizer(
+        request.text,
+        return_tensors="pt",
+        truncation=True,
+        padding=True,
+        max_length=128
+    )
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probabilities = torch.softmax(outputs.logits, dim=1)
+
+    top_probs, top_indices = torch.topk(probabilities, 3)
+
+    results = []
+
+    for prob, idx in zip(top_probs[0], top_indices[0]):
+        results.append({
+            "label": int(idx.item()),
+            "confidence": round(float(prob.item()), 4)
+        })
+
+    return {
+        "input_text": request.text,
+        "top_predictions": results,
+        "final_prediction": results[0]
+    }