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import gradio as gr |
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from transformers import pipeline |
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import pandas as pd |
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import os |
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classifier = pipeline( |
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"text-classification", |
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model="ashishkgpian/biolink_large_disease_classification" |
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) |
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icd9_data = pd.read_csv('D_ICD_DIAGNOSES.csv') |
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icd9_data.columns = ['ROW_ID', 'ICD9_CODE', 'SHORT_TITLE', 'LONG_TITLE'] |
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def preprocessing(test_df): |
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test_df.loc[ |
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test_df['ICD9_CODE'].str.startswith("V"), 'ICD9_CODE'] = test_df.ICD9_CODE.apply( |
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lambda x: x[:4]) |
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test_df.loc[ |
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test_df['ICD9_CODE'].str.startswith("E"), 'ICD9_CODE'] = test_df.ICD9_CODE.apply( |
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lambda x: x[:4]) |
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test_df.loc[(~test_df.ICD9_CODE.str.startswith("E")) & ( |
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~test_df.ICD9_CODE.str.startswith("V")), 'ICD9_CODE'] = test_df.ICD9_CODE.apply( |
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lambda x: x[:3]) |
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return test_df |
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icd9_data = preprocessing(icd9_data) |
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def classify_symptoms(text): |
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try: |
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results = classifier(text, top_k=5) |
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formatted_results = [] |
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for result in results: |
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code = result['label'] |
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code_info = icd9_data[icd9_data['ICD9_CODE'] == code] |
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formatted_results.append({ |
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"ICD9 Code": code, |
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"Short Title": code_info['SHORT_TITLE'].iloc[0] if not code_info.empty else "N/A", |
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"Long Title": code_info['LONG_TITLE'].iloc[0] if not code_info.empty else "N/A", |
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"Confidence": f"{result['score']:.2%}" |
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}) |
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return formatted_results |
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except Exception as e: |
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return f"Error processing classification: {str(e)}" |
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custom_css = """ |
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.gradio-container { |
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width: 100% !important; |
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max-width: 100% !important; |
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margin: 0 !important; |
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padding: 0 !important; |
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min-height: 100vh !important; |
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display: flex !important; |
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flex-direction: column !important; |
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background-color: #000000 !important; |
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color: #ffffff !important; |
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} |
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.main-container { |
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text-align: center; |
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padding: 2rem; |
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margin: 0; |
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background: #000000; |
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width: 100%; |
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color: #ffffff; |
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} |
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.content-wrapper { |
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max-width: 1400px; |
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margin: 0 auto; |
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padding: 0 2rem; |
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width: 100%; |
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box-sizing: border-box; |
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background: #000000; |
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color: #ffffff; |
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} |
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h1 { |
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color: #b388ff !important; |
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font-size: 2.5rem !important; /* Reduced from 3rem */ |
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margin-bottom: 0.5rem !important; |
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font-weight: 700 !important; |
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} |
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h3 { |
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color: #9575cd !important; |
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font-size: 1.2rem !important; /* Reduced from 1.4rem */ |
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font-weight: 500 !important; |
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margin-bottom: 2rem !important; |
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} |
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.input-container { |
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background: #121212 !important; |
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padding: 2rem !important; |
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border-radius: 12px !important; |
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box-shadow: 0 4px 6px rgba(255, 255, 255, 0.05) !important; |
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margin: 2rem 0 !important; |
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width: 100% !important; |
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border: 1px solid #333333 !important; |
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} |
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.input-container label { |
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color: #ffffff !important; |
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font-weight: 600 !important; |
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font-size: 1rem !important; /* Reduced from 1.1rem */ |
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margin-bottom: 0.5rem !important; |
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background: transparent !important; |
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} |
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textarea { |
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background: #1e1e1e !important; |
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color: #ffffff !important; |
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border: 2px solid #673ab7 !important; |
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border-radius: 8px !important; |
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padding: 1rem !important; |
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font-size: 1.1rem !important; /* Reduced from 1.2rem */ |
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min-height: 150px !important; |
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width: 100% !important; |
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} |
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.submit-btn { |
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background-color: #673ab7 !important; |
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color: white !important; |
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padding: 1rem 3rem !important; |
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border-radius: 8px !important; |
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font-size: 1.1rem !important; /* Reduced from 1.2rem */ |
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margin-top: 1.5rem !important; |
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transition: all 0.3s ease !important; |
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width: auto !important; |
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font-weight: 600 !important; |
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border: none !important; |
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} |
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.submit-btn:hover { |
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background-color: #5e35b1 !important; |
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} |
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.output-container { |
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background: #121212 !important; |
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padding: 2rem !important; |
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border-radius: 12px !important; |
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box-shadow: 0 4px 6px rgba(255, 255, 255, 0.05) !important; |
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margin: 2rem 0 !important; |
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width: 100% !important; |
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border: 1px solid #333333 !important; |
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color: #ffffff !important; |
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} |
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.output-container label { |
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color: #ffffff !important; |
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font-weight: 600 !important; |
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font-size: 1rem !important; /* Reduced from 1.1rem */ |
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margin-bottom: 1rem !important; |
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background: transparent !important; |
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} |
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.examples-container { |
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background: #121212 !important; |
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padding: 2rem !important; |
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border-radius: 12px !important; |
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margin: 2rem 0 !important; |
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box-shadow: 0 4px 6px rgba(255, 255, 255, 0.05) !important; |
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width: 100% !important; |
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border: 1px solid #333333 !important; |
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color: #ffffff !important; |
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} |
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.examples-container label { |
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color: #ffffff !important; |
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font-weight: 600 !important; |
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font-size: 1rem !important; /* Reduced from 1.1rem */ |
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background: transparent !important; |
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} |
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.footer { |
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text-align: center; |
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padding: 2rem; |
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background: #000000; |
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margin-top: auto; |
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width: 100%; |
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border-top: 1px solid #333333; |
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color: #ffffff; |
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} |
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""" |
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with gr.Blocks(css=custom_css) as demo: |
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with gr.Row(elem_classes=["main-container"]): |
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with gr.Column(elem_classes=["content-wrapper"]): |
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gr.Markdown( |
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""" |
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# 🏥 Clinical Symptom ICD9 Classifier |
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### AI-Powered Medical Diagnosis Code Suggestion Tool |
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""" |
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) |
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with gr.Row(elem_classes=["input-output-row"]): |
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with gr.Column(elem_classes=["input-container"]): |
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gr.Markdown("Clinical Symptom Description") |
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with gr.Column(elem_classes=["inner-input-container"]): |
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input_text = gr.Textbox( |
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show_label=False, |
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placeholder="Enter detailed patient symptoms and clinical observations...", |
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lines=5 |
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) |
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submit_btn = gr.Button("Analyze Symptoms", elem_classes=["submit-btn"]) |
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with gr.Column(elem_classes=["output-container"]): |
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output = gr.JSON( |
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label="Suggested ICD9 Diagnostic Codes with Descriptions" |
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) |
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with gr.Row(elem_classes=["examples-container"]): |
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examples = gr.Examples( |
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examples=[ |
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["45-year-old male experiencing severe chest pain, radiating to left arm, with shortness of breath and excessive sweating"], |
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["Persistent headache for 2 weeks, accompanied by dizziness and occasional blurred vision"], |
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["Diabetic patient reporting frequent urination, increased thirst, and unexplained weight loss"], |
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["Elderly patient with chronic knee pain, reduced mobility, and signs of inflammation"] |
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], |
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inputs=input_text, |
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label="Example Clinical Cases" |
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) |
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submit_btn.click(fn=classify_symptoms, inputs=input_text, outputs=output) |
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input_text.submit(fn=classify_symptoms, inputs=input_text, outputs=output) |
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with gr.Row(elem_classes=["footer"]): |
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gr.Markdown( |
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""" |
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⚕️ <strong>Medical Disclaimer:</strong> This AI tool is designed to assist medical professionals in ICD9 code classification. |
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Always verify suggestions with clinical judgment and consult appropriate medical resources. |
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""" |
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) |
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if __name__ == "__main__": |
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demo.launch(share=True) |
