| import os |
| import streamlit as st |
| from transformers.models.bert import BertTokenizer, BertForSequenceClassification |
| import torch |
| import pickle |
| import random |
| from collections import defaultdict |
| import json |
|
|
| |
| def load_name_encoder(): |
| file_path = os.path.join(os.getcwd(), "best_model", "name_encoder.pkl") |
| if not os.path.exists(file_path): |
| st.error(f"Name encoder faylı tapılmadı: {file_path}") |
| st.stop() |
| with open(file_path, "rb") as f: |
| name_encoder = pickle.load(f) |
| return name_encoder |
|
|
| |
| @st.cache_resource |
| def load_model(): |
| name_encoder = load_name_encoder() |
| model_path = os.path.join(os.getcwd(), "best_model") |
|
|
| tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') |
| model = BertForSequenceClassification.from_pretrained( |
| model_path, |
| num_labels=len(name_encoder.classes_) |
| ) |
| model.eval() |
| return tokenizer, model, name_encoder |
|
|
| |
| def predict_disease(symptoms_text, tokenizer, model, name_encoder): |
| symptoms = [s.strip() for s in symptoms_text.split(",") if s.strip()] |
| agg_probs = defaultdict(float) |
| n_shuffles = 10 |
|
|
| for _ in range(n_shuffles): |
| random.shuffle(symptoms) |
| shuffled_text = ", ".join(symptoms) |
| 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 = name_encoder.classes_[idx] |
| results.append({"disease": label, "probability": prob}) |
|
|
| return results |
|
|
| |
| st.set_page_config(page_title="Disease API", layout="wide") |
|
|
| |
| query_params = st.query_params |
| is_api_mode = str(query_params.get("api", ["false"])[0]).lower() == "true" |
|
|
| |
| tokenizer, model, name_encoder = load_model() |
|
|
| |
| if is_api_mode: |
| symptoms = query_params.get("symptoms", [""])[0] |
| if symptoms: |
| results = predict_disease(symptoms, tokenizer, model, name_encoder) |
| api_response = { |
| "status": "success", |
| "input": symptoms, |
| "predictions": results |
| } |
| else: |
| api_response = { |
| "status": "error", |
| "message": "symptoms parameter required" |
| } |
|
|
| st.markdown( |
| f"```json\n{json.dumps(api_response, ensure_ascii=False, indent=2)}\n```" |
| ) |
| st.stop() |
|
|
| |
| st.title("🏥 Disease Prediction") |
| st.success("Model yükləndi!") |
|
|
| |
| st.write("Available classes:", list(name_encoder.classes_)) |
|
|
| |
| st.markdown("### API İstifadəsi") |
| space_url = "https://your-username-your-space-name.hf.space" |
| api_example = f"{space_url}/?api=true&symptoms=fever,cough,headache" |
| st.code(api_example) |
|
|
| |
| with st.form(key="predict_form"): |
| text = st.text_area("Simptomları daxil edin (vergüllə ayırın):") |
| submit_button = st.form_submit_button(label="Predict") |
|
|
| if submit_button: |
| if not text.strip(): |
| st.warning("Simptomları daxil edin!") |
| else: |
| results = predict_disease(text, tokenizer, model, name_encoder) |
| st.subheader("🔍 Nəticələr:") |
| for result in results: |
| st.write(f"**{result['disease']}** — {result['probability']*100:.2f}%") |
|
|
