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from transformers import BertTokenizer, BertForSequenceClassification
import torch
import pickle
from collections import defaultdict
import random
import os
from safetensors.torch import load_file
# Model və label_encoder yüklənməsi
def load_model():
try:
# Label encoder
with open("best_model/label_encoder.pkl", "rb") as f:
label_encoder = pickle.load(f)
# Tokenizer
tokenizer = BertTokenizer.from_pretrained("best_model")
# Model (safetensors avtomatik dəstəklənir)
model = BertForSequenceClassification.from_pretrained("best_model", use_safetensors=True)
model.eval()
print(f"Model uğurla yükləndi. Label sayı: {len(label_encoder.classes_)}")
return tokenizer, model, label_encoder
except Exception as e:
print(f"Model yüklənmə xətası: {e}")
# Faylları yoxla
if os.path.exists("best_model"):
files = os.listdir("best_model")
print(f"best_model qovluğundakı fayllar: {files}")
else:
print("best_model qovluğu mövcud deyil")
return None, None, None
# Model yükləmə
tokenizer, model, label_encoder = load_model()
# Prediction funksiyası
def predict_disease(text):
if tokenizer is None or model is None or label_encoder is None:
return "❌ Model yüklənməyib! Xəta var."
if not text.strip():
return "⚠️ Please enter some symptoms!"
symptoms = [s.strip() for s in text.split(",") if s.strip()]
if not symptoms:
return "⚠️ Please enter valid symptoms separated by commas!"
try:
agg_probs = defaultdict(float)
n_shuffles = 10
for i 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 idx, p in enumerate(probs):
agg_probs[idx] += p.item()
# Ortalama hesabla
for k in agg_probs:
agg_probs[k] /= n_shuffles
# Top 3 nəticə
top_3 = sorted(agg_probs.items(), key=lambda x: x[1], reverse=True)[:3]
results = ["🏥 Top 3 Predicted Diseases:\n"]
for idx, prob in top_3:
label = label_encoder.classes_[idx]
results.append(f"• **{label}** — Probability: {prob*100:.2f}%")
return "\n".join(results)
except Exception as e:
return f"❌ Prediction xətası: {str(e)}"
# Gradio interface
iface = gr.Interface(
fn=predict_disease,
inputs=gr.Textbox(
lines=2,
placeholder="fever, cough, headache, shortness of breath",
label="Enter your symptoms (comma separated)"
),
outputs=gr.Textbox(label="Predicted Diseases"),
title="🏥 Disease NLP Classifier",
description="Enter your symptoms separated by commas and get top 3 predicted diseases with confidence scores.",
examples=[
["fever, cough, headache"],
["stomach pain, nausea, vomiting"],
["chest pain, shortness of breath"],
["dizziness, fatigue, weakness"],
["skin rash, itching, redness"]
]
)
# Launch
if __name__ == "__main__":
if tokenizer and model and label_encoder:
print("✅ Model hazırdır, Gradio başladılır...")
iface.launch(
server_name="0.0.0.0",
server_port=int(os.environ.get("PORT", 7860)),
share=True # Public link yaradır
)
else:
print("❌ Model yüklənmədi, Gradio başladıla bilmir!")
print("\nDebug məlumatları:")
print(f"Hazırkı qovluq: {os.getcwd()}")
print(f"Qovluq məzmunu: {os.listdir('.')}")
# Sadə debug interface
def debug_info():
return f"Debug məlumatları:\nHazırkı qovluq: {os.getcwd()}\nFayllar: {os.listdir('.')}"
debug_iface = gr.Interface(
fn=debug_info,
inputs=gr.Textbox(placeholder="Debug üçün hər hansı mətn yazın"),
outputs=gr.Textbox(),
title="🔧 Debug Interface"
)
debug_iface.launch(
server_name="0.0.0.0",
server_port=int(os.environ.get("PORT", 7860))
) |