Upload app.py

app.py

@@ -11,7 +11,7 @@ from sklearn.metrics import classification_report, confusion_matrix, ConfusionMa
 import warnings
 warnings.filterwarnings("ignore")
 
-df = pd.read_csv("pre_deployment_mental_health_dataset_balanced.csv")
+df = pd.read_csv("/Users/hassan/Desktop/pre_deployment_mental_health_dataset_balanced.csv")
 df.head()
 
 print(f"Shape of dataset: {df.shape}")
@@ -134,6 +134,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 
 # Load the dataset
+df = pd.read_csv("pre_deployment_mental_health_dataset_balanced.csv")
 df_clean = df.drop(columns=["Soldier_ID"])
 
 # Encode categorical variables
@@ -152,55 +153,156 @@ X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_sta
 model = LogisticRegression(max_iter=1000)
 model.fit(X_train, y_train)
 
-# Gradio prediction function
-def predict_risk(age, gender, rank, service_years, deployment_count, mental_issues, sleep, stress, anxiety, depression, fitness, support, substance_use, combat_intensity, family_status):
-    input_df = pd.DataFrame([{
-        "Age": age,
-        "Gender": label_encoders["Gender"].transform([gender])[0],
-        "Rank": label_encoders["Rank"].transform([rank])[0],
-        "Service_Years": service_years,
-        "Deployment_Count": deployment_count,
-        "Previous_Mental_Health_Issues": label_encoders["Previous_Mental_Health_Issues"].transform([mental_issues])[0],
-        "Sleep_Hours": sleep,
-        "Stress_Level": stress,
-        "Anxiety_Score": anxiety,
-        "Depression_Score": depression,
-        "Physical_Fitness_Score": fitness,
-        "Social_Support_Score": support,
-        "Substance_Use": label_encoders["Substance_Use"].transform([substance_use])[0],
-        "Combat_Training_Intensity": label_encoders["Combat_Training_Intensity"].transform([combat_intensity])[0],
-        "Family_Status": label_encoders["Family_Status"].transform([family_status])[0]
-    }])
-    
+# Create test profile dropdown (first 5 samples)
+sample_data = df.head(5).set_index("Soldier_ID").drop(columns=["Risk_Level"])
+
+# Define prediction function
+def predict_risk(*args):
+    input_dict = dict(zip(X.columns, args))
+
+    # Encode string values before passing to model
+    for col in input_dict:
+        if col in label_encoders:
+            val = input_dict[col]
+            # If value is not already a known label, skip encoding
+            if val not in label_encoders[col].classes_:
+                # Decode the most frequent value instead
+                fallback = label_encoders[col].classes_[0]
+                input_dict[col] = label_encoders[col].transform([fallback])[0]
+            else:
+                input_dict[col] = label_encoders[col].transform([val])[0]
+
+
+    input_df = pd.DataFrame([input_dict])
     pred = model.predict(input_df)[0]
     risk_label = label_encoders["Risk_Level"].inverse_transform([pred])[0]
     return f"🔎 Predicted Mental Health Risk: **{risk_label}**"
 
-# Gradio Interface
-demo = gr.Interface(
-    fn=predict_risk,
-    inputs=[
-        gr.Slider(20, 60, step=1, label="Age"),
-        gr.Radio(["Male", "Female"], label="Gender"),
-        gr.Dropdown(["E1", "E2", "E3", "E4", "E5", "O1", "O2", "O3"], label="Rank"),
-        gr.Slider(1, 25, step=1, label="Service Years"),
-        gr.Slider(0, 10, step=1, label="Deployment Count"),
-        gr.Radio(["Yes", "No"], label="Previous Mental Health Issues"),
-        gr.Slider(0.0, 10.0, step=0.1, label="Sleep Hours"),
-        gr.Slider(0, 10, step=1, label="Stress Level"),
-        gr.Slider(0, 21, step=1, label="Anxiety Score"),
-        gr.Slider(0, 27, step=1, label="Depression Score"),
-        gr.Slider(1.0, 10.0, step=0.1, label="Physical Fitness Score"),
-        gr.Slider(1.0, 10.0, step=0.1, label="Social Support Score"),
-        gr.Radio(["Yes", "No"], label="Substance Use"),
-        gr.Radio(["Low", "Moderate", "High"], label="Combat Training Intensity"),
-        gr.Radio(["Single", "Married", "Divorced", "Engaged"], label="Family Status")
-    ],
-    outputs="text",
-    title="🧠 Mental Health Risk Predictor (Military)",
-    description="Enter a soldier's pre-deployment details to estimate mental health risk level using logistic regression."
-)
-
-# Run the app
-demo.launch()
 
+def autofill(soldier_id):
+    row = sample_data.loc[soldier_id].copy()
+    return row.tolist()
+
+
+
+
+# Create Gradio interface
+with gr.Blocks(title="Military Mental Health Risk Predictor") as demo:
+    gr.Markdown("# 🧠 Mental Health Risk Predictor (Military)")
+    gr.Markdown("Predict a soldier's mental health risk using full or quick input modes.")
+
+    with gr.Tabs():
+        # ----------------- Tab 1: Full Input Mode -----------------
+        with gr.Tab("Full Input Mode"):
+            gr.Markdown("### 📋 Full Feature Input")
+
+            dropdown = gr.Dropdown(choices=list(sample_data.index), label="Auto-Fill From Soldier ID")
+            autofill_btn = gr.Button("Auto-Fill")
+
+            # Original full input fields
+            age = gr.Slider(20, 60, step=1, label="Age")
+            gender = gr.Radio(["Male", "Female"], label="Gender")
+            rank = gr.Dropdown(["E1", "E2", "E3", "E4", "E5", "O1", "O2", "O3"], label="Rank")
+            service_years = gr.Slider(1, 25, step=1, label="Service Years")
+            deployment_count = gr.Slider(0, 10, step=1, label="Deployment Count")
+            mental_issues = gr.Radio(["Yes", "No"], label="Previous Mental Health Issues")
+            sleep = gr.Slider(0.0, 10.0, step=0.1, label="Sleep Hours")
+            stress = gr.Slider(0, 10, step=1, label="Stress Level")
+            anxiety = gr.Slider(0, 21, step=1, label="Anxiety Score")
+            depression = gr.Slider(0, 27, step=1, label="Depression Score")
+            fitness = gr.Slider(1.0, 10.0, step=0.1, label="Physical Fitness Score")
+            support = gr.Slider(1.0, 10.0, step=0.1, label="Social Support Score")
+            substance_use = gr.Radio(["Yes", "No"], label="Substance Use")
+            combat_intensity = gr.Radio(["Low", "Moderate", "High"], label="Combat Training Intensity")
+            family_status = gr.Radio(["Single", "Married", "Divorced", "Engaged"], label="Family Status")
+
+            output_full = gr.Textbox(label="Prediction", interactive=False)
+
+            all_inputs = [
+                age, gender, rank, service_years, deployment_count,
+                mental_issues, sleep, stress, anxiety, depression,
+                fitness, support, substance_use, combat_intensity, family_status
+            ]
+
+            gr.Button("Submit").click(
+                fn=predict_risk, 
+                inputs=all_inputs, 
+                outputs=output_full
+            )
+
+            autofill_btn.click(
+                fn=autofill, 
+                inputs=dropdown, 
+                outputs=all_inputs
+            )
+
+            gr.Markdown("⚠️ Tip: Use the dropdown to quickly fill test data.")
+
+            gr.Markdown("""
+### 🟢🟠🔴 Risk Level Legend
+
+- 🟢 **Low Risk**: Soldier shows strong mental and physical indicators. Fit for deployment without concern.
+- 🟠 **Moderate Risk**: Some factors suggest reduced resilience or emerging stress. Monitor or support as needed.
+- 🔴 **High Risk**: Significant signs of mental strain. Recommend further evaluation or support intervention.
+""")
+
+
+        # ----------------- Tab 2: Quick Input Mode -----------------
+        with gr.Tab("Quick Input Mode"):
+            gr.Markdown("### ⚡ Quick Mode (Important Features Only)")
+
+            stress_q = gr.Slider(0, 10, step=1, label="Stress Level")
+            anxiety_q = gr.Slider(0, 21, step=1, label="Anxiety Score")
+            depression_q = gr.Slider(0, 27, step=1, label="Depression Score")
+            fitness_q = gr.Slider(1.0, 10.0, step=0.1, label="Physical Fitness Score")
+            support_q = gr.Slider(1.0, 10.0, step=0.1, label="Social Support Score")
+            sleep_q = gr.Slider(0.0, 10.0, step=0.1, label="Sleep Hours")
+            service_q = gr.Slider(1, 25, step=1, label="Service Years")
+
+            quick_inputs = [stress_q, anxiety_q, depression_q, fitness_q, support_q, sleep_q, service_q]
+
+            output_quick = gr.Textbox(label="Prediction", interactive=False)
+
+            def predict_quick(stress, anxiety, depression, fitness, support, sleep, service):
+                input_dict = {
+                    "Stress_Level": stress,
+                    "Anxiety_Score": anxiety,
+                    "Depression_Score": depression,
+                    "Physical_Fitness_Score": fitness,
+                    "Social_Support_Score": support,
+                    "Sleep_Hours": sleep,
+                    "Service_Years": service
+                }
+
+                # Fill missing fields
+                for col in X.columns:
+                    if col not in input_dict:
+                        if col in label_encoders:
+                            mode_encoded = X[col].mode()[0]
+                            input_dict[col] = label_encoders[col].inverse_transform([mode_encoded])[0]
+                        else:
+                            input_dict[col] = X[col].median()
+
+                # ENSURE THE ORDER MATCHES X.columns
+                ordered_values = [input_dict[col] for col in X.columns]
+                return predict_risk(*ordered_values)
+
+
+
+
+            gr.Button("Submit").click(
+                fn=predict_quick,
+                inputs=quick_inputs,
+                outputs=output_quick
+            )
+
+            gr.Markdown("""
+        ### 🟢🟠🔴 Risk Level Legend
+
+        - 🟢 **Low Risk**: Soldier shows strong mental and physical indicators. Fit for deployment without concern.
+        - 🟠 **Moderate Risk**: Some factors suggest reduced resilience or emerging stress. Monitor or support as needed.
+        - 🔴 **High Risk**: Significant signs of mental strain. Recommend further evaluation or support intervention.
+        """)
+
+
+demo.launch()