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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("alibidaran/Symptom2disease") | |
| model = AutoModelForSequenceClassification.from_pretrained("alibidaran/Symptom2disease") | |
| label_2id={'Psoriasis': 0, | |
| 'Varicose Veins': 1, | |
| 'Typhoid': 2, | |
| 'Chicken pox': 3, | |
| 'Impetigo': 4, | |
| 'Dengue': 5, | |
| 'Fungal infection': 6, | |
| 'Common Cold': 7, | |
| 'Pneumonia': 8, | |
| 'Dimorphic Hemorrhoids': 9, | |
| 'Arthritis': 10, | |
| 'Acne': 11, | |
| 'Bronchial Asthma': 12, | |
| 'Hypertension': 13, | |
| 'Migraine': 14, | |
| 'Cervical spondylosis': 15, | |
| 'Jaundice': 16, | |
| 'Malaria': 17, | |
| 'urinary tract infection': 18, | |
| 'allergy': 19, | |
| 'gastroesophageal reflux disease': 20, | |
| 'drug reaction': 21, | |
| 'peptic ulcer disease': 22, | |
| 'diabetes': 23} | |
| id2_label={i:v for v,i in label_2id.items()} | |
| def detect_symptom(symptoms): | |
| inputs_id=tokenizer(symptoms,padding=True,truncation=True,return_tensors="pt") | |
| output=model(inputs_id['input_ids']) | |
| preds=torch.nn.functional.softmax(output.logits,-1).topk(5) | |
| results={id2_label[preds.indices[0][i].item()]:preds.values[0][i].item() for i in range(5)} | |
| return results | |
| demo=gr.Interface(fn=detect_symptom,inputs='text',outputs=gr.Label(num_top_classes=5), | |
| examples=["I can't stop sneezing and I feel really tired and crummy. My throat is really sore", | |
| "I have been experiencing a severe headache that is accompanied by pain behind my eyes.", | |
| "There are small red spots all over my body that I can't explain. It's worrying me.", | |
| "I've been having a really hard time going to the bathroom lately. It's really painful"]) | |
| demo.launch() |