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from fastapi import FastAPI, Query
from pydantic import BaseModel
from typing import List
from transformers import BertTokenizer, BertForSequenceClassification
import torch
import pickle
import random
from collections import defaultdict
app = FastAPI()
# Model və tokenizer yükləmə
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained("best_model")
model.eval()
with open("best_model/label_encoder.pkl", "rb") as f:
label_encoder = pickle.load(f)
class PredictionResponse(BaseModel):
disease: str
probability: float
@app.get("/predict", response_model=List[PredictionResponse])
def predict(symptoms: str = Query(..., description="Comma-separated symptoms")):
symptoms_list = [s.strip() for s in symptoms.split(",") if s.strip()]
agg_probs = defaultdict(float)
n_shuffles = 10
for _ in range(n_shuffles):
random.shuffle(symptoms_list)
shuffled_text = ", ".join(symptoms_list)
inputs = tokenizer(shuffled_text, return_tensors="pt", truncation=True, padding=True, max_length=128)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1).squeeze()
for i, p in enumerate(probs):
agg_probs[i] += p.item()
for k in agg_probs:
agg_probs[k] /= n_shuffles
top_3 = sorted(agg_probs.items(), key=lambda x: x[1], reverse=True)[:3]
results = []
for idx, prob in top_3:
label = label_encoder.classes_[idx]
results.append({"disease": label, "probability": float(prob)})
return results
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