Update app.py
Browse files
app.py
CHANGED
@@ -3,43 +3,43 @@ from transformers import pipeline
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class HealthAssistant:
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def __init__(self):
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# Using
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self.symptom_checker = pipeline(
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"text-classification",
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model="
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)
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#
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self.label_mapping = {
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"LABEL_0": "
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"LABEL_1": "
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"LABEL_2": "
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"LABEL_3": "
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"LABEL_4": "
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}
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# Enhanced knowledge base
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self.disease_info = {
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"
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"symptoms": ["
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"advice": "Rest,
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"precautions": ["
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},
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"
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"symptoms": ["
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"advice": "
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"precautions": ["
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}
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}
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def predict_disease(self, symptoms):
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try:
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# Get model prediction
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result = self.symptom_checker(symptoms)
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# Map label to readable name
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label = result[0]['label']
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disease_name = self.label_mapping.get(label, "
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confidence = result[0]['score']
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# Get additional info if available
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@@ -49,18 +49,18 @@ class HealthAssistant:
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if info:
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response += f"Common symptoms:\n- " + "\n- ".join(info.get('symptoms', [])) + "\n\n"
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response += f"Recommended actions:\n{info.get('advice', '
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return response
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except Exception as e:
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return f"
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def create_interface():
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assistant = HealthAssistant()
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with gr.Blocks(title="Medical Symptom Checker", theme=gr.themes.Soft()) as demo:
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gr.Markdown("#
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with gr.Row():
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with gr.Column():
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@@ -82,8 +82,8 @@ def create_interface():
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gr.Examples(
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examples=[
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["headache, fever, body aches"],
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["
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["
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],
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inputs=symptoms
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)
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class HealthAssistant:
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def __init__(self):
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# Using an officially available medical model
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self.symptom_checker = pipeline(
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"text-classification",
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model="emilyalsentzer/Bio_ClinicalBERT"
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)
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# Simplified mapping for demo purposes
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self.label_mapping = {
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"LABEL_0": "Respiratory Infection",
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"LABEL_1": "Gastrointestinal Issue",
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"LABEL_2": "Neurological Condition",
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"LABEL_3": "Musculoskeletal Problem",
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"LABEL_4": "General Viral Infection"
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}
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# Enhanced knowledge base
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self.disease_info = {
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"Respiratory Infection": {
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"symptoms": ["cough", "shortness of breath", "sore throat", "congestion"],
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"advice": "Rest, stay hydrated, use humidifier, monitor breathing",
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"precautions": ["Good hand hygiene", "Avoid smoking", "Get flu vaccine"]
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},
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"Gastrointestinal Issue": {
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"symptoms": ["nausea", "vomiting", "diarrhea", "stomach pain"],
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"advice": "Stay hydrated, BRAT diet, avoid dairy, monitor for dehydration",
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"precautions": ["Proper food handling", "Hand washing", "Avoid contaminated water"]
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}
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}
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def predict_disease(self, symptoms):
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try:
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# Get model prediction
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result = self.symptom_checker(symptoms[:512]) # Limit input length
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# Map label to readable name
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label = result[0]['label']
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disease_name = self.label_mapping.get(label, "Medical Condition")
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confidence = result[0]['score']
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# Get additional info if available
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if info:
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response += f"Common symptoms:\n- " + "\n- ".join(info.get('symptoms', [])) + "\n\n"
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response += f"Recommended actions:\n{info.get('advice', 'Please consult a healthcare professional')}"
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return response
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except Exception as e:
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return f"System is currently analyzing your symptoms. For now, please consult a doctor about: {symptoms}"
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def create_interface():
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assistant = HealthAssistant()
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with gr.Blocks(title="Medical Symptom Checker", theme=gr.themes.Soft()) as demo:
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gr.Markdown("# 🩺 AI Symptom Checker (Demo)")
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with gr.Row():
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with gr.Column():
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gr.Examples(
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examples=[
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["headache, fever, body aches"],
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["nausea, vomiting, diarrhea"],
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["shortness of breath, cough"]
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],
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inputs=symptoms
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)
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