Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import pipeline
+
+# Initialize the pipeline
+pipe = pipeline(task="ner", 
+                model='Mykes/rubert_ner_SDDCS', 
+                tokenizer='Mykes/rubert_ner_SDDCS', 
+                aggregation_strategy='max')
+
+def process_text(text):
+    # Convert input to lowercase as in your example
+    results = pipe(text.lower())
+    
+    # Format the output
+    output = []
+    for entity in results:
+        formatted_result = f"Type: {entity['entity_group']}\nWord: {entity['word']}\nScore: {entity['score']:.4f}\n"
+        output.append(formatted_result)
+    
+    return "\n".join(output)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=process_text,
+    inputs=gr.Textbox(lines=3, placeholder="Enter your text here..."),
+    outputs=gr.Textbox(lines=10),
+    title="Medical NER for Russian Text",
+    description="This model identifies medical entities (diseases, symptoms, drugs, etc.) in Russian text.",
+    examples=[
+        ["У ребенка треога и норушения сна, потеря сознания, раньше ставили паническое расстройство. по назначению психиатра принимал атаракс без эффекта."],
+    ]
+)
+
+# Launch the interface
+iface.launch()