Upload app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from transformers import TFAutoModel, AutoTokenizer
+import numpy as np
+import shap
+from scipy.special import softmax
+
+# Model and Tokenizer Setup
+MODEL_NAME = "distilbert-base-uncased-finetuned-sst-2-english"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = TFAutoModel.from_pretrained(MODEL_NAME)
+
+# Constants
+SEQ_LEN = 128
+CONDITIONS = [
+    "Common Cold", "COVID-19", "Allergies", "Anxiety Disorder", "Skin Infection",
+    "Heart Condition", "Digestive Issues", "Migraine", "Muscle Strain", "Arthritis"
+]
+
+# Dynamic Condition Predictions
+def predict_condition(description: str):
+    tokens = tokenizer(
+        description, max_length=SEQ_LEN, truncation=True, padding="max_length", return_tensors="tf"
+    )
+    outputs = model(tokens).last_hidden_state[:, 0, :]  # CLS token output
+    scores = softmax(outputs.numpy())
+    predictions = dict(zip(CONDITIONS, scores.flatten()))
+    return predictions
+
+# Lifestyle Tips
+LIFESTYLE_TIPS = {
+    "Common Cold": "Rest, stay hydrated, and use saline nasal sprays.",
+    "COVID-19": "Quarantine, stay hydrated, and seek medical attention if symptoms worsen.",
+    "Allergies": "Avoid allergens, take antihistamines, and use air purifiers.",
+    "Anxiety Disorder": "Practice mindfulness, exercise, and seek therapy if needed.",
+    "Skin Infection": "Keep the area clean, use topical creams, and consult a dermatologist.",
+    # Add more conditions and tips...
+}
+
+def get_lifestyle_advice(condition: str):
+    return LIFESTYLE_TIPS.get(condition, "Consult a healthcare professional for guidance.")
+
+# Interactive Health Visualization (SHAP)
+def explain_prediction(text: str):
+    explainer = shap.Explainer(lambda x: predict_condition(x), tokenizer)
+    shap_values = explainer([text])
+    return shap.plots.text(shap_values, display=False)
+
+# Symptom Tracker (Simple Implementation)
+symptom_history = []
+
+def log_symptom(symptom: str):
+    symptom_history.append(symptom)
+    return f"Logged: {symptom}. Total symptoms logged: {len(symptom_history)}"
+
+def display_symptom_trends():
+    return "\n".join(symptom_history[-10:])  # Last 10 logged symptoms
+
+# Gradio UI Design
+css = """
+textarea { background-color: transparent; border: 1px solid #6366f1; }
+"""
+with gr.Blocks(title="MedAI Compass", css=css, theme=gr.themes.Soft()) as app:
+    # Header
+    gr.HTML("<h1>MedAI Compass: Comprehensive Symptom and Health Guide</h1>")
+
+    # Section: Symptom Diagnosis
+    with gr.Row():
+        gr.Markdown("## Symptom Diagnosis")
+        input_description = gr.Textbox(label="Describe your symptom")
+        diagnose_btn = gr.Button("Diagnose")
+        diagnosis_output = gr.Label(label="Possible Conditions")
+        diagnose_btn.click(predict_condition, inputs=input_description, outputs=diagnosis_output)
+
+    # Section: SHAP Analysis
+    with gr.Row():
+        gr.Markdown("## Explain Predictions")
+        shap_text_input = gr.Textbox(label="Enter Symptom Description for Analysis")
+        shap_btn = gr.Button("Generate Explanation")
+        shap_output = gr.HTML()
+        shap_btn.click(explain_prediction, inputs=shap_text_input, outputs=shap_output)
+
+    # Section: Personalized Advice
+    with gr.Row():
+        gr.Markdown("## Personalized Health Advice")
+        condition_input = gr.Dropdown(choices=CONDITIONS, label="Select a Condition")
+        advice_output = gr.Textbox(label="Advice")
+        advice_btn = gr.Button("Get Advice")
+        advice_btn.click(get_lifestyle_advice, inputs=condition_input, outputs=advice_output)
+
+    # Section: Symptom Tracker
+    with gr.Row():
+        gr.Markdown("## Symptom Tracker")
+        tracker_input = gr.Textbox(label="Log a Symptom")
+        tracker_btn = gr.Button("Log Symptom")
+        tracker_output = gr.Textbox(label="Logged Symptoms")
+        tracker_btn.click(log_symptom, inputs=tracker_input, outputs=tracker_output)
+        tracker_display_btn = gr.Button("Display Trends")
+        tracker_trends_output = gr.Textbox(label="Symptom Trends")
+        tracker_display_btn.click(display_symptom_trends, outputs=tracker_trends_output)
+
+    # Footer
+    gr.HTML("<p>© 2024 MedAI Compass. All Rights Reserved.</p>")
+
+app.launch()