Update app.py

app.py

@@ -12,6 +12,11 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipe
 import googlemaps
 import folium
 import torch
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import accuracy_score
 
 # Suppress TensorFlow warnings
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
@@ -46,9 +51,67 @@ model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/e
 # Google Maps API Client
 gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
 
-# Helper Functions
+# Disease Prediction Code
+def load_data():
+    df = pd.read_csv("Training.csv")
+    tr = pd.read_csv("Testing.csv")
+    
+    disease_dict = {
+        # Disease encoding dictionary...
+    }
+    df.replace({'prognosis': disease_dict}, inplace=True)
+    df = df.infer_objects(copy=False)
+
+    tr.replace({'prognosis': disease_dict}, inplace=True)
+    tr = tr.infer_objects(copy=False)
+    
+    return df, tr, disease_dict
+
+df, tr, disease_dict = load_data()
+l1 = list(df.columns[:-1])  
+X = df[l1]
+y = df['prognosis']
+X_test = tr[l1]
+y_test = tr['prognosis']
+
+def train_models():
+    models = {
+        "Decision Tree": DecisionTreeClassifier(),
+        "Random Forest": RandomForestClassifier(),
+        "Naive Bayes": GaussianNB()
+    }
+    trained_models = {}
+    for model_name, model_obj in models.items():
+        model_obj.fit(X, y)
+        acc = accuracy_score(y_test, model_obj.predict(X_test))
+        trained_models[model_name] = (model_obj, acc)
+    return trained_models
+
+trained_models = train_models()
+
+def predict_disease(model, symptoms):
+    input_test = np.zeros(len(l1))
+    for symptom in symptoms:
+        if symptom in l1:
+            input_test[l1.index(symptom)] = 1
+    prediction = model.predict([input_test])[0]
+    return list(disease_dict.keys())[list(disease_dict.values()).index(prediction)]
+
+def disease_prediction_interface(symptoms):
+    symptoms_selected = [s for s in symptoms if s != "None"]
+    
+    if len(symptoms_selected) < 3:
+        return "Please select at least 3 symptoms for accurate prediction."
+    
+    results = []
+    for model_name, (model, acc) in trained_models.items():
+        prediction = predict_disease(model, symptoms_selected)
+        results.append(f"{model_name} Prediction: Predicted Disease: **{prediction}** (Accuracy: **{acc * 100:.2f}%**)")
+    
+    return results
+
+# Helper Functions (for chatbot)
 def bag_of_words(s, words):
-    """Convert user input to bag-of-words vector."""
     bag = [0] * len(words)
     s_words = word_tokenize(s)
     s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()]
@@ -59,7 +122,6 @@ def bag_of_words(s, words):
     return np.array(bag)
 
 def generate_chatbot_response(message, history):
-    """Generate chatbot response and maintain conversation history."""
     history = history or []
     try:
         result = chatbot_model.predict([bag_of_words(message, words)])
@@ -75,7 +137,6 @@ def generate_chatbot_response(message, history):
     return history, response
 
 def analyze_sentiment(user_input):
-    """Analyze sentiment and map to emojis."""
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
         outputs = model_sentiment(**inputs)
@@ -84,7 +145,6 @@ def analyze_sentiment(user_input):
     return f"Sentiment: {sentiment_map[sentiment_class]}"
 
 def detect_emotion(user_input):
-    """Detect emotions based on input."""
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
     emotion = result[0]["label"].lower().strip()
@@ -99,52 +159,20 @@ 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 = {
-        "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"],
-        ],
+        # Suggestions based on emotion...
     }
     
-    # Format the output to include HTML anchor tags
     formatted_suggestions = [
         [title, f'<a href="{link}" target="_blank">{link}</a>'] for title, link in suggestions.get(emotion_key, [["No specific suggestions available.", "#"]])
     ]
-
     return formatted_suggestions
 
 def get_health_professionals_and_map(location, query):
-    """Search nearby healthcare professionals using Google Maps API."""
     try:
         if not location or not query:
-            return [], ""  # Return empty list if inputs are missing
-
+            return [], ""
         geo_location = gmaps.geocode(location)
         if geo_location:
             lat, lng = geo_location[0]["geometry"]["location"].values()
@@ -152,35 +180,42 @@ def get_health_professionals_and_map(location, query):
             professionals = []
             map_ = folium.Map(location=(lat, lng), zoom_start=13)
             for place in places_result:
-                # Use a list of values to append each professional
                 professionals.append([place['name'], place.get('vicinity', 'No address provided')])
                 folium.Marker(
                     location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
                     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
+        return [], ""
 
 # Main Application Logic
-def app_function(user_input, location, query, history):
+def app_function(user_input, location, query, symptoms, history):
     chatbot_history, _ = generate_chatbot_response(user_input, history)
     sentiment_result = analyze_sentiment(user_input)
     emotion_result, cleaned_emotion = detect_emotion(user_input)
     suggestions = generate_suggestions(cleaned_emotion)
     professionals, map_html = get_health_professionals_and_map(location, query)
-    return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html
+    disease_results = disease_prediction_interface(symptoms)
+    
+    return (
+        chatbot_history, 
+        sentiment_result, 
+        emotion_result, 
+        suggestions, 
+        professionals, 
+        map_html,
+        disease_results
+    )
 
 # CSS Styling
 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;
@@ -190,7 +225,6 @@ h1 {
     text-align: center;
     font-size: 2.5rem;
 }
-
 textarea, input {
     background: transparent;
     color: black;
@@ -201,20 +235,12 @@ textarea, input {
     outline: none;
     border-radius: 8px;
 }
-
 textarea:focus, input:focus {
     background: transparent;
     color: black;
     border: 2px solid orange;
     outline: none;
 }
-
-textarea:hover, input:hover {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-}
-
 .df-container {
     background: white;
     color: black;
@@ -226,26 +252,21 @@ textarea:hover, input:hover {
     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;
 }
@@ -256,26 +277,35 @@ 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="Please Enter Your Current Location Here")
-        query = gr.Textbox(label="Please Enter Which Health Professional You Want To Search Nearby")
+        location = gr.Textbox(label="Your Current Location Here")
+        query = gr.Textbox(label="Search Health Professionals Nearby")
         
+    # New Row for Disease Prediction Symptoms
+    with gr.Row():
+        symptom1 = gr.Dropdown(choices=["None"] + l1, label="Symptom 1")
+        symptom2 = gr.Dropdown(choices=["None"] + l1, label="Symptom 2")
+        symptom3 = gr.Dropdown(choices=["None"] + l1, label="Symptom 3")
+        symptom4 = gr.Dropdown(choices=["None"] + l1, label="Symptom 4")
+        symptom5 = gr.Dropdown(choices=["None"] + l1, label="Symptom 5")
+
     submit = gr.Button(value="Submit", variant="primary")
 
     chatbot = gr.Chatbot(label="Chat History")
     sentiment = gr.Textbox(label="Detected Sentiment")
     emotion = gr.Textbox(label="Detected Emotion")
     
-    # Adding Suggestions Title with Styled Markdown (Centered and Bold)
+    # Suggestions Title
     gr.Markdown("Suggestions", elem_id="suggestions-title")
     
-    suggestions = gr.DataFrame(headers=["Title", "Link"])  # Table for suggestions
-    professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])  # Changed to DataFrame
+    suggestions = gr.DataFrame(headers=["Title", "Link"])  # Suggestions DataFrame
+    professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])  # Professionals DataFrame
     map_html = gr.HTML(label="Interactive Map")
+    disease_predictions = gr.Textbox(label="Disease Predictions")  # For Disease Prediction Results
 
     submit.click(
         app_function,
-        inputs=[user_input, location, query, chatbot],
-        outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html],
+        inputs=[user_input, location, query, [symptom1, symptom2, symptom3, symptom4, symptom5], chatbot],
+        outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html, disease_predictions],
     )
 
 app.launch()