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README.md

@@ -1,12 +1,13 @@
 ---
-title: Symptom Cheking
-emoji: 🚀
-colorFrom: purple
-colorTo: blue
+title: CDSS
+emoji: 📚
+colorFrom: blue
+colorTo: yellow
 sdk: gradio
 sdk_version: 4.28.3
 app_file: app.py
 pinned: false
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import BertForSequenceClassification, BertTokenizer
+from sklearn.preprocessing import LabelEncoder
+import torch
+import numpy as np
+
+
+# Load model and label encoder
+fine_tuned_model = BertForSequenceClassification.from_pretrained('./fine_tuned_model')
+label_encoder = LabelEncoder()
+label_encoder.classes_ = np.load('./label_encoder_classes.npy', allow_pickle=True)
+tokenizer = BertTokenizer.from_pretrained("./tokenizer")  # Load tokenizer from local file
+
+def predict(symptom):
+    user_input_encoding = tokenizer(symptom, padding=True, truncation=True, return_tensors='pt', max_length=512, return_attention_mask=True, return_token_type_ids=True)
+
+    with torch.no_grad():
+        logits = fine_tuned_model(**user_input_encoding)
+        probabilities = torch.nn.functional.softmax(logits.logits, dim=1).numpy()[0]
+        predicted_labels = np.argsort(-probabilities)[:5]
+        predicted_diseases = label_encoder.inverse_transform(predicted_labels)
+        predicted_probabilities = probabilities[predicted_labels]
+
+    predictions = [{'disease': disease, 'probability': probability * 100} for disease, probability in zip(predicted_diseases, predicted_probabilities)]
+    return predictions
+
+# Gradio Interface
+demo = gr.Interface(fn=predict, inputs="text", outputs="text")
+demo.launch(share=True)  # Share your demo with just 1 extra parameter 🚀

fine_tuned_model/config.json

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+{
+  "_name_or_path": "dmis-lab/biobert-v1.1",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
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+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "hidden_size": 768,
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gitignore

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gr.py

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+import os
+import gradio as gr
+
+# Define your proxy server address and port
+proxy_url = "http://22.110.0.225:10809"
+
+# Set the proxy environment variables
+os.environ['HTTP_PROXY'] = proxy_url
+os.environ['HTTPS_PROXY'] = proxy_url
+
+def greet(name):
+    return "Hello " + name + "!"
+
+# Create the Gradio interface
+demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
+
+# Launch the interface
+demo.launch(share=True)

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+}

tokenizer/tokenizer_special_tokens_map.json

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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}