| import gradio as gr
|
| from transformers import AutoTokenizer, AutoModelForSequenceClassification
|
| import torch
|
| import torch.nn.functional as F
|
|
|
|
|
| model_path = "./biobert_model"
|
| model = AutoModelForSequenceClassification.from_pretrained(model_path)
|
| tokenizer = AutoTokenizer.from_pretrained(model_path)
|
| model.eval()
|
|
|
| labels = ["No interaction", "Mild", "Moderate", "Severe"]
|
|
|
| def predict_interaction(text):
|
| encoding = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
|
| with torch.no_grad():
|
| outputs = model(**encoding)
|
| probs = F.softmax(outputs.logits, dim=1)
|
| pred = torch.argmax(probs, dim=1).item()
|
| return f"🧠 Prediction: {labels[pred]}"
|
|
|
| gr.Interface(fn=predict_interaction, inputs="text", outputs="text", title="Drug Interaction Predictor").launch()
|
|
|
