Create gradio_app.py

gradio_app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# --- 1. Load Disease Prediction Model ---
+with open("disease_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+with open("symptoms.pkl", "rb") as f:
+    all_symptoms = pickle.load(f)
+
+# Preprocess symptoms
+all_symptoms = sorted(all_symptoms)
+display_symptoms = [s.replace('_', ' ').title() for s in all_symptoms]
+label_to_symptom = dict(zip(display_symptoms, all_symptoms))
+
+# --- 2. Medical Knowledge Base ---
+MEDICAL_KNOWLEDGE = {
+
+    "migraine": [
+        "For migraines: (1) Rest in dark room (2) OTC pain relievers (ibuprofen/acetaminophen) (3) Apply cold compress (4) Consult neurologist if frequent",
+        "Migraine treatment options include triptans (prescription) and caffeine. Avoid triggers like bright lights or strong smells."
+    ],
+
+    "allergy": [
+        "Allergy management: (1) Antihistamines (cetirizine/loratadine) (2) Nasal sprays (3) Allergy shots (immunotherapy) for severe cases",
+        "For food allergies: Strict avoidance, carry epinephrine auto-injector (EpiPen), read food labels carefully"
+    ],
+    "cold": [
+        "Treat colds with rest, fluids, and OTC pain relievers. See doctor if fever lasts >3 days",
+        "Most colds resolve in 7-10 days. Use decongestants for nasal congestion"
+    ],
+    "headache": [
+        "For headaches: Hydrate, rest, and use OTC pain relievers sparingly",
+        "Persistent headaches require medical evaluation - consult your doctor"
+    ],
+    "fever": [
+        "For fever: Rest, fluids, and acetaminophen/ibuprofen. Seek help if >39°C or lasts >3 days",
+        "High fever warning: Seek emergency care if fever >40°C or with stiff neck"
+    ]
+}
+
+SPECIAL_RESPONSES = {
+    "general approaches": "I can provide specific guidance for: allergies, migraines, colds, fever, back pain, rashes. What condition are you asking about?",
+    "consult a doctor": "For these symptoms, seek medical care: severe pain, difficulty breathing, sudden weakness, high fever (>103°F), or symptoms lasting >7 days"
+}
+
+def get_medical_response(user_query):
+    user_query = user_query.lower()
+
+    # First check for special cases
+    for phrase, response in SPECIAL_RESPONSES.items():
+        if phrase in user_query:
+            return response
+
+    # Then check medical conditions
+    for condition, responses in MEDICAL_KNOWLEDGE.items():
+        if condition in user_query:
+            return np.random.choice(responses)
+
+    # Final improvement - suggest related conditions
+    related = [cond for cond in MEDICAL_KNOWLEDGE.keys() if cond in user_query]
+    if related:
+        return f"Are you asking about {', '.join(related)}? {np.random.choice(MEDICAL_KNOWLEDGE[related[0]])}"
+
+    return "I can advise on: " + ", ".join(MEDICAL_KNOWLEDGE.keys()) + ". Please be more specific."
+
+# --- 3. Disease Prediction Function ---
+def predict_disease(selected_labels):
+    if not selected_labels or len(selected_labels) < 4:
+        return "⚠️ Please select at least 4 symptoms for accurate results."
+
+    user_symptoms = [label_to_symptom[label] for label in selected_labels]
+    input_vector = [1 if symptom in user_symptoms else 0 for symptom in all_symptoms]
+    input_vector = np.array([input_vector])
+    probas = model.predict_proba(input_vector)[0]
+    max_proba = np.max(probas)
+    predicted = model.classes_[np.argmax(probas)]
+
+    sorted_indices = np.argsort(probas)[::-1]
+    top_diseases = [
+        f"<b>{i+1}. {model.classes_[idx]}</b> — {probas[idx]*100:.1f}%"
+        for i, idx in enumerate(sorted_indices[:3])
+    ]
+
+    prediction_result = (
+        f"<div style='background: #001a33; padding: 15px; border-radius: 8px; margin-bottom: 15px;'>"
+        f"<h3 style='color: #4fc3f7; margin-top: 0;'>🩺 Predicted Disease</h3>"
+        f"<p style='font-size: 18px; color: white;'>{predicted} <span style='color: #4fc3f7'>({max_proba*100:.1f}% confidence)</span></p>"
+        "</div>"
+        "<div style='background: #001a33; padding: 15px; border-radius: 8px;'>"
+        "<h3 style='color: #4fc3f7; margin-top: 0;'>🔍 Top 3 Possible Diseases</h3>"
+        "<ul style='color: white; padding-left: 20px;'>" +
+        "".join([f"<li>{d}</li>" for d in top_diseases]) +
+        "</ul>"
+        "</div>"
+    )
+    return prediction_result
+
+# --- 4. Chat Responder ---
+def chatbot_respond(message, chat_history):
+    response = get_medical_response(message)
+    return chat_history + [(message, response)], ""
+
+# --- 5. UI Setup ---
+custom_css = """
+:root {
+    --primary: #4fc3f7;
+    --secondary: #001a33;
+    --text: #ffffff;
+    --bg: #0a192f;
+    --card-bg: #0a2342;
+    --error: #ff6b6b;
+}
+body, .gradio-container {
+    background: var(--bg) !important;
+    color: var(--text) !important;
+    font-family: 'Segoe UI', Roboto, sans-serif;
+}
+/* [Keep all your existing CSS styles] */
+"""
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("""
+    <div style="text-align: center; margin-bottom: 20px;">
+        <h1 style="margin-bottom: 5px;">🧬 Medical Diagnosis Assistant</h1>
+        <p style="color: #4fc3f7; font-size: 16px;">Select symptoms for diagnosis and get medical advice</p>
+    </div>
+    """)
+
+    with gr.Row(equal_height=True):
+        with gr.Column(scale=1, min_width=300):
+            gr.Markdown("### 🔍 Symptom Checker")
+            symptoms_input = gr.CheckboxGroup(
+                choices=display_symptoms,
+                label="Select your symptoms:",
+                interactive=True
+            )
+            predict_btn = gr.Button("Analyze Symptoms", variant="primary")
+            prediction_output = gr.Markdown(
+                label="Diagnosis Results",
+                value="Your results will appear here..."
+            )
+
+        with gr.Column(scale=1, min_width=400):
+            gr.Markdown("### 💬 Medical Advisor")
+            chatbot = gr.Chatbot(
+                label="Chat with Medical Advisor",
+                show_label=False,
+                bubble_full_width=False
+            )
+            with gr.Row():
+                user_input = gr.Textbox(
+                    placeholder="Ask about symptoms or treatments...",
+                    label="",
+                    show_label=False,
+                    container=False,
+                    scale=7
+                )
+                send_btn = gr.Button("Send", scale=1, min_width=80)
+
+    # Event handlers
+    predict_btn.click(
+        fn=predict_disease,
+        inputs=symptoms_input,
+        outputs=prediction_output
+    )
+    send_btn.click(
+        fn=chatbot_respond,
+        inputs=[user_input, chatbot],
+        outputs=[chatbot, user_input]
+    )
+    user_input.submit(
+        fn=chatbot_respond,
+        inputs=[user_input, chatbot],
+        outputs=[chatbot, user_input]
+    )
+
+demo.launch()