Update app.py

app.py

@@ -1,107 +1,265 @@
-import gradio as gr
-import numpy as np
-import pickle
-
-# Load the trained models
-with open("stroke_clf.pkl", "rb") as f:
-    clf_model = pickle.load(f)
-
-with open("stroke_reg.pkl", "rb") as f:
-    reg_model = pickle.load(f)
-
-def predict_stroke(chest_pain, shortness_breath, irregular_heartbeat, fatigue_weakness, 
-                   dizziness, swelling, pain_neck_jaw, excessive_sweating, 
-                   persistent_cough, nausea_vomiting, high_bp, chest_discomfort, 
-                   cold_hands_feet, snoring, anxiety, age):
-    """
-    Prepares the input features and returns stroke risk prediction using both the classification
-    and regression models.
-    """
-    # Convert boolean inputs to 1 (True) or 0 (False), keeping age as numeric.
-    features = [
-        1 if chest_pain else 0,
-        1 if shortness_breath else 0,
-        1 if irregular_heartbeat else 0,
-        1 if fatigue_weakness else 0,
-        1 if dizziness else 0,
-        1 if swelling else 0,
-        1 if pain_neck_jaw else 0,
-        1 if excessive_sweating else 0,
-        1 if persistent_cough else 0,
-        1 if nausea_vomiting else 0,
-        1 if high_bp else 0,
-        1 if chest_discomfort else 0,
-        1 if cold_hands_feet else 0,
-        1 if snoring else 0,
-        1 if anxiety else 0,
-        age
-    ]
-    
-    sample_input = np.array([features])
-    
-    # Get predictions
-    classification_result = clf_model.predict(sample_input)[0]
-    risk_class = "At Risk" if classification_result == 1 else "Not At Risk"
-    
-    regression_result = reg_model.predict(sample_input)[0]
-    risk_percentage = round(regression_result, 2)
-    
-    return (f"**Classification Result:** {risk_class}\n"
-            f"**Regression Result:** {risk_percentage}% stroke risk")
-
-# Build Gradio Interface using Blocks for a clean, organized UI
-with gr.Blocks(css=".output { white-space: pre-wrap; }") as demo:
-    gr.Markdown(
-    """
-    # Stroke Risk Prediction
-    This application uses two machine learning models to predict stroke risk:
-    
-    - **Classification Model:** Determines if an individual is at risk (Yes/No).
-    - **Regression Model:** Estimates the stroke risk percentage.
-    
-    **Provide the following information based on your symptoms:**
-    """
-    )
-    
-    with gr.Row():
-        with gr.Column():
-            chest_pain = gr.Checkbox(label="Chest Pain")
-            shortness_breath = gr.Checkbox(label="Shortness of Breath")
-            irregular_heartbeat = gr.Checkbox(label="Irregular Heartbeat")
-            fatigue_weakness = gr.Checkbox(label="Fatigue & Weakness")
-            dizziness = gr.Checkbox(label="Dizziness")
-            swelling = gr.Checkbox(label="Swelling (Edema)")
-            pain_neck_jaw = gr.Checkbox(label="Pain in Neck/Jaw/Shoulder/Back")
-            excessive_sweating = gr.Checkbox(label="Excessive Sweating")
-        with gr.Column():
-            persistent_cough = gr.Checkbox(label="Persistent Cough")
-            nausea_vomiting = gr.Checkbox(label="Nausea/Vomiting")
-            high_bp = gr.Checkbox(label="High Blood Pressure")
-            chest_discomfort = gr.Checkbox(label="Chest Discomfort (During Activity)")
-            cold_hands_feet = gr.Checkbox(label="Cold Hands/Feet")
-            snoring = gr.Checkbox(label="Snoring/Sleep Apnea")
-            anxiety = gr.Checkbox(label="Anxiety/Feeling of Doom")
-            age = gr.Number(label="Age", value=50)
-    
-    predict_btn = gr.Button("Predict Stroke Risk", variant="primary")
-    output = gr.Markdown(label="Prediction Results", elem_classes="output")
-    
-    predict_btn.click(
-        predict_stroke, 
-        inputs=[
-            chest_pain, shortness_breath, irregular_heartbeat, fatigue_weakness,
-            dizziness, swelling, pain_neck_jaw, excessive_sweating,
-            persistent_cough, nausea_vomiting, high_bp, chest_discomfort,
-            cold_hands_feet, snoring, anxiety, age
-        ],
-        outputs=output
-    )
-    
-    gr.Markdown(
-    """
-    ---
-    **Disclaimer:** This tool is intended for informational purposes only and should not be used as a substitute for professional medical advice.
-    """
-    )
-
-demo.launch()
+import gradio as gr
+import numpy as np
+import pickle
+import time
+
+# Load the trained models
+with open("stroke_clf.pkl", "rb") as f:
+    clf_model = pickle.load(f)
+
+with open("stroke_reg.pkl", "rb") as f:
+    reg_model = pickle.load(f)
+
+def predict_stroke(age, chest_pain, shortness_breath, irregular_heartbeat, fatigue_weakness, 
+                  dizziness, swelling, pain_neck_jaw, excessive_sweating, persistent_cough, 
+                  nausea_vomiting, high_bp, chest_discomfort, cold_hands_feet, snoring, anxiety):
+    """
+    Prepares input features and returns stroke risk prediction using both models.
+    """
+    features = [
+        1 if chest_pain else 0,
+        1 if shortness_breath else 0,
+        1 if irregular_heartbeat else 0,
+        1 if fatigue_weakness else 0,
+        1 if dizziness else 0,
+        1 if swelling else 0,
+        1 if pain_neck_jaw else 0,
+        1 if excessive_sweating else 0,
+        1 if persistent_cough else 0,
+        1 if nausea_vomiting else 0,
+        1 if high_bp else 0,
+        1 if chest_discomfort else 0,
+        1 if cold_hands_feet else 0,
+        1 if snoring else 0,
+        1 if anxiety else 0,
+        age
+    ]
+    
+    sample_input = np.array([features])
+    
+    # Get predictions
+    classification_result = clf_model.predict(sample_input)[0]
+    regression_result = reg_model.predict(sample_input)[0]
+    risk_percentage = round(regression_result, 2)
+    
+    # Simulate processing time for better UX
+    time.sleep(1)
+    
+    # Format the response based on risk level
+    if classification_result == 1:
+        result = f"""
+## 🚨 Risk Assessment Results
+
+**Status:** At Risk
+**Risk Level:** {risk_percentage}%
+
+### Important Next Steps:
+1. 👨‍⚕️ Schedule an immediate appointment with your healthcare provider
+2. 📊 Monitor your blood pressure regularly
+3. 💊 Take prescribed medications as directed
+4. 📝 Maintain a log of any symptoms you experience
+
+### Lifestyle Recommendations:
+- 🏃‍♂️ Start gentle exercise after consulting your doctor
+- 🥗 Follow a heart-healthy diet
+- 😴 Ensure 7-8 hours of quality sleep
+- 🚭 Quit smoking if applicable
+- 🧘‍♂️ Practice stress management techniques
+
+Remember: Early intervention is key to preventing stroke. Please consult a healthcare professional as soon as possible.
+"""
+    else:
+        result = f"""
+## ✅ Risk Assessment Results
+
+**Status:** Low Risk
+**Risk Level:** {risk_percentage}%
+
+### Maintaining Your Health:
+1. 🏃‍♂️ Continue regular exercise
+2. 🥗 Maintain a balanced diet
+3. 🩺 Schedule regular check-ups
+4. 💧 Stay hydrated
+5. 😌 Manage stress levels
+
+### Preventive Measures:
+- 🌟 Regular physical activity (150 minutes/week)
+- 🍎 Five servings of fruits and vegetables daily
+- 💤 Consistent sleep schedule
+- 🧘‍♂️ Regular relaxation practices
+- 📊 Annual health screenings
+
+Keep up the good work in maintaining your health!
+"""
+    
+    return result
+
+# Create the Gradio interface with a more interactive layout
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="purple")) as demo:
+    # Header
+    gr.Markdown(
+    """
+    # **Welcome to EarlyMed Stroke Risk Prediction Tool! 🏥🧠**
+    
+    Welcome to **EarlyMed**—an initiative by our team at **VIT-AP University**, dedicated to empowering you with early health insights. 
+    **Leveraging AI for early detection**, our mission is simple: ✨ **"Early Detection, Smarter Decision."**
+    
+    As part of our commitment to preventive healthcare, we have developed the **EarlyMed Stroke Risk Prediction Tool** 
+    to help you assess your stroke risk before consulting a doctor. This tool is designed to provide **timely insights** 
+    and encourage proactive health decisions.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            # Basic Information
+            gr.Markdown("### 👤 Basic Information")
+            age = gr.Slider(label="What is your age?", minimum=18, maximum=100, step=1, value=50)
+            
+            # Primary Symptoms
+            gr.Markdown("### 🫀 Primary Symptoms")
+            chest_pain = gr.Radio(
+                label="First of all, are you experiencing any chest pain?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            shortness_breath = gr.Radio(
+                label="Do you feel short of breath during normal activities?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            irregular_heartbeat = gr.Radio(
+                label="Have you noticed irregular heartbeats?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            
+        with gr.Column():
+            # Secondary Symptoms
+            gr.Markdown("### 🤒 Additional Symptoms")
+            fatigue_weakness = gr.Radio(
+                label="Now I am gonna check some additional features. Do you feel unusual fatigue or weakness?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            dizziness = gr.Radio(
+                label="Do you experience dizziness?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            swelling = gr.Radio(
+                label="Have you noticed swelling in your extremities?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            
+    with gr.Row():
+        with gr.Column():
+            # Additional Health Indicators
+            gr.Markdown("### 🏥 Health Indicators")
+            pain_neck_jaw = gr.Radio(
+                label="Do you have pain in neck, jaw, or shoulders?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            excessive_sweating = gr.Radio(
+                label="Do you experience excessive sweating?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            persistent_cough = gr.Radio(
+                label="Do you have a persistent cough?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            
+        with gr.Column():
+            # Risk Factors
+            gr.Markdown("### ⚠️ Risk Factors")
+            nausea_vomiting = gr.Radio(
+                label="Do you experience nausea or vomiting?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            high_bp = gr.Radio(
+                label="Have you been diagnosed with high blood pressure?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            chest_discomfort = gr.Radio(
+                label="Do you feel chest discomfort during activities?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            
+    with gr.Row():
+        with gr.Column():
+            # Lifestyle Factors
+            gr.Markdown("### 🌟 Lifestyle Factors")
+            cold_hands_feet = gr.Radio(
+                label="Do your hands or feet often feel cold?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            snoring = gr.Radio(
+                label="Do you snore or have sleep apnea?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+            anxiety = gr.Radio(
+                label="Do you experience frequent anxiety?",
+                choices=["No", "Yes"],
+                value="No"
+            )
+    
+    # Submit Button
+    submit_btn = gr.Button("Analyze My Risk", variant="primary")
+    
+    # Output
+    output = gr.Markdown(label="Risk Assessment Results")
+    
+    # Process inputs
+    def process_inputs(*args):
+        # Convert "Yes"/"No" to boolean
+        bool_args = [arg == "Yes" for arg in args[1:]]
+        return predict_stroke(args[0], *bool_args)
+    
+    submit_btn.click(
+        process_inputs,
+        inputs=[age, chest_pain, shortness_breath, irregular_heartbeat, 
+                fatigue_weakness, dizziness, swelling, pain_neck_jaw,
+                excessive_sweating, persistent_cough, nausea_vomiting,
+                high_bp, chest_discomfort, cold_hands_feet, snoring, anxiety],
+        outputs=output
+    )
+    
+    # Information Section
+    gr.Markdown(
+    """
+    ---
+    ### **How Does This Work? 🤖**
+    Our AI model analyzes key health factors—such as **age, blood pressure, lifestyle habits, and medical history**—to predict your stroke risk with high accuracy. The tool uses **two approaches**:
+    
+    🔹 **Classification Model:** Determines whether you are at risk or not.
+    🔹 **Regression Model:** Estimates your stroke risk percentage.
+    
+    ### **What to Do If Your Stroke Risk is High? 🚨**
+    If the tool predicts a high risk of stroke, don't panic! Here's what you should do:
+    
+    ✅ **Consult a Doctor:** Share your results with a healthcare professional for expert advice.
+    ✅ **Monitor Your Health:** Keep track of your **blood pressure, cholesterol levels, and overall fitness.**
+    ✅ **Adopt a Healthy Lifestyle:** Regular exercise, a balanced diet, and quitting smoking can significantly reduce stroke risks.
+    ✅ **Manage Chronic Conditions:** If you have **diabetes or hypertension**, ensure they are well-controlled.
+    
+    💡 **Important:** This tool is meant for **early awareness** and is **not a substitute for medical diagnosis**. Always consult a doctor for professional assessment and guidance.
+    
+    Stay informed, stay healthy, and take control of your well-being! 💙💪
+    
+    ---
+    
+    *© 2025 EarlyMed - VIT-AP University*
+    
+    **Disclaimer:** This tool is for informational purposes only and should not be used as a substitute for professional medical advice, diagnosis, or treatment.
+    """
+    )
+
+demo.launch()