Update app.py

app.py

@@ -57,17 +57,17 @@ def load_data():
     try:
         df = pd.read_csv("Training.csv")
         tr = pd.read_csv("Testing.csv")
-    except FileNotFoundError:
-        raise RuntimeError("Data files not found. Please ensure `Training.csv` and `Testing.csv` are uploaded correctly.")
+    except FileNotFoundError as e:
+        raise RuntimeError("Data files not found. Please ensure `Training.csv` and `Testing.csv` are uploaded correctly.") from e
 
     # Encode diseases
     disease_dict = {
         'Fungal infection': 0, 'Allergy': 1, 'GERD': 2, 'Chronic cholestasis': 3, 'Drug Reaction': 4,
-        'Peptic ulcer diseae': 5, 'AIDS': 6, 'Diabetes ': 7, 'Gastroenteritis': 8, 'Bronchial Asthma': 9,
-        'Hypertension ': 10, 'Migraine': 11, 'Cervical spondylosis': 12, 'Paralysis (brain hemorrhage)': 13,
-        'Jaundice': 14, 'Malaria': 15, 'Chicken pox': 16, 'Dengue': 17, 'Typhoid': 18, 'hepatitis A': 19,
+        'Peptic ulcer disease': 5, 'AIDS': 6, 'Diabetes': 7, 'Gastroenteritis': 8, 'Bronchial Asthma': 9,
+        'Hypertension': 10, 'Migraine': 11, 'Cervical spondylosis': 12, 'Paralysis (brain hemorrhage)': 13,
+        'Jaundice': 14, 'Malaria': 15, 'Chicken pox': 16, 'Dengue': 17, 'Typhoid': 18, 'Hepatitis A': 19,
         'Hepatitis B': 20, 'Hepatitis C': 21, 'Hepatitis D': 22, 'Hepatitis E': 23, 'Alcoholic hepatitis': 24,
-        'Tuberculosis': 25, 'Common Cold': 26, 'Pneumonia': 27, 'Dimorphic hemmorhoids(piles)': 28,
+        'Tuberculosis': 25, 'Common Cold': 26, 'Pneumonia': 27, 'Dimorphic hemorrhoids(piles)': 28,
         'Heart attack': 29, 'Varicose veins': 30, 'Hypothyroidism': 31, 'Hyperthyroidism': 32,
         'Hypoglycemia': 33, 'Osteoarthritis': 34, 'Arthritis': 35,
         '(vertigo) Paroxysmal Positional Vertigo': 36, 'Acne': 37, 'Urinary tract infection': 38,
@@ -81,23 +81,21 @@ def load_data():
     print("Unique values in prognosis after mapping:", df['prognosis'].unique())
 
     # Ensure prognosis is purely numerical after mapping
-    if df['prognosis'].dtype == 'object':  # Check for unmapped entries
+    if df['prognosis'].dtype == 'object':
         raise ValueError(f"The prognosis contains unmapped values: {df['prognosis'].unique()}")
 
-    df['prognosis'] = df['prognosis'].astype(int)  # Convert to integer
-    df = df.infer_objects()  # Remove 'copy' argument
+    df['prognosis'] = df['prognosis'].astype(int)
+    df = df.infer_objects()
 
     # Similar process for the testing data
     tr.replace({'prognosis': disease_dict}, inplace=True)
-
-    # Check unique values in testing data
     print("Unique values in prognosis for testing data after mapping:", tr['prognosis'].unique())
     
-    if tr['prognosis'].dtype == 'object':  # Check for unmapped entries
+    if tr['prognosis'].dtype == 'object':
         raise ValueError(f"Testing data prognosis contains unmapped values: {tr['prognosis'].unique()}")
 
-    tr['prognosis'] = tr['prognosis'].astype(int)  # Convert to integer if necessary
-    tr = tr.infer_objects()  # Remove 'copy' argument
+    tr['prognosis'] = tr['prognosis'].astype(int)
+    tr = tr.infer_objects()
 
     return df, tr, disease_dict
 
@@ -110,9 +108,9 @@ y_test = tr['prognosis']
 
 # Encode the target variable with LabelEncoder if still in string format
 le = LabelEncoder()
-y_encoded = le.fit_transform(y)  # Encode string labels into integers
+y_encoded = le.fit_transform(y)
 
-def train_models():
+def train_models(X, y_encoded, X_test, y_test):
     models = {
         "Decision Tree": DecisionTreeClassifier(),
         "Random Forest": RandomForestClassifier(),
@@ -120,12 +118,15 @@ def train_models():
     }
     trained_models = {}
     for model_name, model_obj in models.items():
-        model_obj.fit(X, y_encoded)  # Use encoded labels
-        acc = accuracy_score(y_test, model_obj.predict(X_test))
-        trained_models[model_name] = (model_obj, acc)
+        try:
+            model_obj.fit(X, y_encoded)  # Fit the model
+            acc = accuracy_score(y_test, model_obj.predict(X_test))
+            trained_models[model_name] = (model_obj, acc)
+        except Exception as e:
+            print(f"Failed to train {model_name}: {e}")
     return trained_models
 
-trained_models = train_models()
+trained_models = train_models(X, y_encoded, X_test, y_test)
 
 def predict_disease(model, symptoms):
     input_test = np.zeros(len(l1))
@@ -205,11 +206,39 @@ def detect_emotion(user_input):
     return emotion_map.get(emotion, "Unknown 🤔"), emotion
 
 def generate_suggestions(emotion):
+    """Return relevant suggestions based on detected emotions."""
     emotion_key = emotion.lower()
     suggestions = {
-        # Define suggestions based on the detected emotion
+        "joy": [
+            ["Relaxation Techniques", "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"],
+            ["Dealing with Stress", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
+            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
+            ["Relaxation Video", "https://youtu.be/m1vaUGtyo-A"],
+        ],
+        "anger": [
+            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
+            ["Stress Management Tips", "https://www.health.harvard.edu/health-a-to-z"],
+            ["Dealing with Anger", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
+            ["Relaxation Video", "https://youtu.be/MIc299Flibs"],
+        ],
+        "fear": [
+            ["Mindfulness Practices", "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"],
+            ["Coping with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
+            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
+            ["Relaxation Video", "https://youtu.be/yGKKz185M5o"],
+        ],
+        "sadness": [
+            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
+            ["Dealing with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
+            ["Relaxation Video", "https://youtu.be/-e-4Kx5px_I"],
+        ],
+        "surprise": [
+            ["Managing Stress", "https://www.health.harvard.edu/health-a-to-z"],
+            ["Coping Strategies", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
+            ["Relaxation Video", "https://youtu.be/m1vaUGtyo-A"],
+        ],
     }
-    
+
     formatted_suggestions = [
         [title, f'<a href="{link}" target="_blank">{link}</a>'] for title, link in suggestions.get(emotion_key, [["No specific suggestions available.", "#"]])
     ]
@@ -234,9 +263,10 @@ def get_health_professionals_and_map(location, query):
                     popup=f"{place['name']}"
                 ).add_to(map_)
             return professionals, map_._repr_html_()
-        return [], ""  # Return empty list if no professionals found
+        return [], ""
     except Exception as e:
-        return [], ""  # Return empty list on exception
+        print(f"Failed to fetch healthcare professionals: {e}")
+        return [], ""
 
 # Main Application Logic
 def app_function(user_input, location, query, symptoms, history):
@@ -261,10 +291,9 @@ def app_function(user_input, location, query, symptoms, history):
 custom_css = """
 body {
     font-family: 'Roboto', sans-serif;
-    background-color: #3c6487;  /* Set the background color */
+    background-color: #3c6487; 
     color: white;
 }
-
 h1 {
     background: #ffffff;
     color: #000000;
@@ -274,7 +303,6 @@ h1 {
     text-align: center;
     font-size: 2.5rem;
 }
-
 textarea, input {
     background: transparent;
     color: black;
@@ -285,14 +313,12 @@ textarea, input {
     outline: none;
     border-radius: 8px;
 }
-
 textarea:focus, input:focus {
     background: transparent;
     color: black;
     border: 2px solid orange;
     outline: none;
 }
-
 .df-container {
     background: white;
     color: black;
@@ -304,26 +330,21 @@ textarea:focus, input:focus {
     height: auto;
     overflow-y: auto;
 }
-
 #suggestions-title {
-    text-align: center !important; /* Ensure the centering is applied */
-    font-weight: bold !important; /* Ensure bold is applied */
-    color: white !important; /* Ensure color is applied */
-    font-size: 4.2rem !important; /* Ensure font size is applied */
-    margin-bottom: 20px !important; /* Ensure margin is applied */
+    text-align: center !important;
+    font-weight: bold !important;
+    color: white !important; 
+    font-size: 4.2rem !important; 
+    margin-bottom: 20px !important; 
 }
-
-/* Style for the submit button */
 .gr-button {
-    background-color: #ae1c93;  /* Set the background color to #ae1c93 */
+    background-color: #ae1c93; 
     box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1), 0 2px 4px rgba(0, 0, 0, 0.06);
     transition: background-color 0.3s ease;
 }
-
 .gr-button:hover {
     background-color: #8f167b;
 }
-
 .gr-button:active {
     background-color: #7f156b;
 }
@@ -334,9 +355,9 @@ with gr.Blocks(css=custom_css) as app:
     gr.HTML("<h1>🌟 Well-Being Companion</h1>")
     
     with gr.Row():
-        user_input = gr.Textbox(label="Please Enter Your Message Here")
-        location = gr.Textbox(label="Your Current Location Here")
-        query = gr.Textbox(label="Search Health Professionals Nearby")
+        user_input = gr.Textbox(label="Please Enter Your Message Here", placeholder="Type your message...")
+        location = gr.Textbox(label="Your Current Location Here", placeholder="Enter location...")
+        query = gr.Textbox(label="Search Health Professionals Nearby", placeholder="What are you looking for...")
         
     with gr.Row():
         symptom1 = gr.Dropdown(choices=["None"] + l1, label="Symptom 1")
@@ -353,10 +374,10 @@ with gr.Blocks(css=custom_css) as app:
     
     gr.Markdown("Suggestions", elem_id="suggestions-title")
     
-    suggestions = gr.DataFrame(headers=["Title", "Link"])  # Suggestions DataFrame
-    professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])  # Professionals DataFrame
+    suggestions = gr.DataFrame(headers=["Title", "Link"])  
+    professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])  
     map_html = gr.HTML(label="Interactive Map")
-    disease_predictions = gr.Textbox(label="Disease Predictions")  # For Disease Prediction Results
+    disease_predictions = gr.Textbox(label="Disease Predictions")  
 
     submit.click(
         app_function,