Create app.py

app.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import tensorflow as tf
+from transformers import TFAutoModel, AutoTokenizer
+import os
+import numpy as np
+
+model_name = 'cardiffnlp/twitter-roberta-base-sentiment-latest'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = tf.keras.models.load_model(
+    "/content/SpecX2/model.h5",
+    custom_objects={
+        'TFRobertaModel': TFAutoModel.from_pretrained(model_name)
+    }
+)
+
+labels = [
+    'Cardiologist',
+    'Dermatologist',
+    'ENT Specialist',
+    'Gastro-enterologist',
+    'General-Physicians',
+    'Neurologist/Gastro-enterologist', 
+    'Ophthalmologist',
+    'Orthopedist', 
+    'Psychiatrist', 
+    'Respirologist',
+    'Rheumatologist',
+    'Rheumatologist/Gastro-enterologist', 
+    'Rheumatologist/Orthopedist',
+    'Surgeon'
+]
+seq_len = 152
+
+def prep_data(text):
+    tokens = tokenizer(
+        text, max_length=seq_len, truncation=True, 
+        padding='max_length', 
+        add_special_tokens=True, 
+        return_tensors='tf'
+    )
+    return {
+        'input_ids': tokens['input_ids'], 
+        'attention_mask': tokens['attention_mask']
+    }
+
+def inference(text):
+    encoded_text = prep_data(text)
+    probs = model.predict_on_batch(encoded_text)
+    probabilities = {i:j for i,j in zip(labels, list(probs.flatten()))}
+    return probabilities
+
+css = """
+textarea {
+    background-color: #00000000;
+    border: 1px solid #6366f160;
+    color: #000000;
+}
+"""
+with gr.Blocks(title="SpecX", css=css, theme=gr.themes.Soft()) as demo:
+  with gr.Row():
+    textmd = gr.Markdown('''
+    <div style="margin: 50px 0;"></div>  
+
+    <h1 style="width:100%; text-align: center;">SpecX: Find the Right Specialist For Your Symptoms!</h1>
+
+    ''')
+  with gr.Row():
+    with gr.Column(scale=1, min_width=600):
+      text_box = gr.Textbox(label="Explain your problem in one sentence.")
+      submit_btn = gr.Button("Submit", elem_id="warningk", variant='primary')
+      examples = gr.Examples(examples=[
+          "When I remember her I feel down",
+          "The area around my heart doesn't feel good.",
+          "I have a split on my thumb that will not heal."
+          ], inputs=text_box)
+    label = gr.Label(num_top_classes=4, label="Recommended Specialist")
+    submit_btn.click(inference, inputs=text_box, outputs=label)
+
+demo.launch()