Update app.py

app.py

@@ -1,281 +1,285 @@
-import os
-import gradio as gr
-import nltk
-import numpy as np
-import tflearn
-import random
-import json
-import pickle
-from nltk.tokenize import word_tokenize
-from nltk.stem.lancaster import LancasterStemmer
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-import googlemaps
-import folium
-import torch
-
-# Suppress TensorFlow warnings
-os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-
-# Download necessary NLTK resources
-nltk.download("punkt")
-stemmer = LancasterStemmer()
-
-# Load intents and chatbot training data
-with open("intents.json") as file:
-    intents_data = json.load(file)
-
-with open("data.pickle", "rb") as f:
-    words, labels, training, output = pickle.load(f)
-
-# Build the chatbot model
-net = tflearn.input_data(shape=[None, len(training[0])])
-net = tflearn.fully_connected(net, 8)
-net = tflearn.fully_connected(net, 8)
-net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
-net = tflearn.regression(net)
-chatbot_model = tflearn.DNN(net)
-chatbot_model.load("MentalHealthChatBotmodel.tflearn")
-
-# Hugging Face sentiment and emotion models
-tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
-model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
-tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
-model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
-
-# Google Maps API Client
-gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
-
-# Helper Functions
-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()]
-    for se in s_words:
-        for i, w in enumerate(words):
-            if w == se:
-                bag[i] = 1
-    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)])
-        tag = labels[np.argmax(result)]
-        response = "I'm sorry, I didn't understand that. 🤔"
-        for intent in intents_data["intents"]:
-            if intent["tag"] == tag:
-                response = random.choice(intent["responses"])
-                break
-    except Exception as e:
-        response = f"Error: {e}"
-    history.append((message, response))
-    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)
-    sentiment_class = torch.argmax(outputs.logits, dim=1).item()
-    sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
-    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()
-    emotion_map = {
-        "joy": "Joy 😊",
-        "anger": "Anger 😠",
-        "sadness": "Sadness 😢",
-        "fear": "Fear 😨",
-        "surprise": "Surprise 😲",
-        "neutral": "Neutral 😐",
-    }
-    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"],
-        ],
-    }
-    
-    # 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
-
-        geo_location = gmaps.geocode(location)
-        if geo_location:
-            lat, lng = geo_location[0]["geometry"]["location"].values()
-            places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]
-            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
-    except Exception as e:
-        return [], ""  # Return empty list on exception
-
-# Main Application Logic
-def app_function(user_input, location, query, 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
-
-# CSS Styling
-custom_css = """
-body {
-    font-family: 'Roboto', sans-serif;
-    background-color: #3c6487; /* Set the background color */
-    color: white;
-}
-
-h1 {
-    background: #ffffff;
-    color: #000000;
-    border-radius: 8px;
-    padding: 10px;
-    font-weight: bold;
-    text-align: center;
-    font-size: 2.5rem;
-}
-
-textarea, input {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-    padding: 8px;
-    font-size: 1rem;
-    caret-color: black;
-    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;
-    border: 2px solid orange;
-    border-radius: 10px;
-    padding: 10px;
-    font-size: 14px;
-    max-height: 400px;
-    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 */
-}
-
-/* Style for the submit button */
-.gr-button {
-    background-color: #ae1c93;  /* Set the background color to #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;
-}
-"""
-
-# Gradio Application
-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")
-        
-    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)
-    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
-    map_html = gr.HTML(label="Interactive Map")
-
-    submit.click(
-        app_function,
-        inputs=[user_input, location, query, chatbot],
-        outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html],
-    )
-
+import os
+import gradio as gr
+import nltk
+import numpy as np
+import tflearn
+import random
+import json
+import pickle
+from nltk.tokenize import word_tokenize
+from nltk.stem.lancaster import LancasterStemmer
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import googlemaps
+import folium
+import torch
+
+# Suppress TensorFlow warnings
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+# Download necessary NLTK resources
+nltk.download("punkt")
+stemmer = LancasterStemmer()
+
+# Load intents and chatbot training data
+with open("intents.json") as file:
+    intents_data = json.load(file)
+
+with open("data.pickle", "rb") as f:
+    words, labels, training, output = pickle.load(f)
+
+# Build the chatbot model
+net = tflearn.input_data(shape=[None, len(training[0])])
+net = tflearn.fully_connected(net, 8)
+net = tflearn.fully_connected(net, 8)
+net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
+net = tflearn.regression(net)
+chatbot_model = tflearn.DNN(net)
+chatbot_model.load("MentalHealthChatBotmodel.tflearn")
+
+# Hugging Face sentiment and emotion models
+tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+
+# Google Maps API Client
+gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
+
+# Helper Functions
+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()]
+    for se in s_words:
+        for i, w in enumerate(words):
+            if w == se:
+                bag[i] = 1
+    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)])
+        tag = labels[np.argmax(result)]
+        response = "I'm sorry, I didn't understand that. 🤔"
+        for intent in intents_data["intents"]:
+            if intent["tag"] == tag:
+                response = random.choice(intent["responses"])
+                break
+    except Exception as e:
+        response = f"Error: {e}"
+    history.append((message, response))
+    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)
+    sentiment_class = torch.argmax(outputs.logits, dim=1).item()
+    sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
+    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()
+    emotion_map = {
+        "joy": "Joy 😊",
+        "anger": "Anger 😠",
+        "sadness": "Sadness 😢",
+        "fear": "Fear 😨",
+        "surprise": "Surprise 😲",
+        "neutral": "Neutral 😐",
+    }
+    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"],
+        ],
+    }
+    
+    # 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
+
+        geo_location = gmaps.geocode(location)
+        if geo_location:
+            lat, lng = geo_location[0]["geometry"]["location"].values()
+            places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]
+            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
+    except Exception as e:
+        return [], ""  # Return empty list on exception
+
+# Main Application Logic
+def app_function(user_input, location, query, 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
+
+# CSS Styling
+custom_css = """
+body {
+    font-family: 'Roboto', sans-serif;
+    background-color: #3c6487; /* Set the background color */
+    color: white;
+}
+
+h1 {
+    background: #ffffff;
+    color: #000000;
+    border-radius: 8px;
+    padding: 10px;
+    font-weight: bold;
+    text-align: center;
+    font-size: 2.5rem;
+}
+
+textarea, input {
+    background: transparent;
+    color: black;
+    border: 2px solid orange;
+    padding: 8px;
+    font-size: 1rem;
+    caret-color: black;
+    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;
+    border: 2px solid orange;
+    border-radius: 10px;
+    padding: 10px;
+    font-size: 14px;
+    max-height: 400px;
+    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 */
+}
+
+/* Style for the submit button */
+.gr-button {
+    background-color: #ae1c93;  /* Set the background color to #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;
+}
+"""
+# Gradio Application
+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")
+
+    # New Predict Disease Button
+    predict_disease = gr.Button(value="Predict Disease", variant="secondary")
+    predict_disease.click(lambda: None, _js="window.open('https://huggingface.co/spaces/Mishal23/wellBeing', '_blank')")
+
+    # Existing Submit Button
+    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)
+    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
+    map_html = gr.HTML(label="Interactive Map")
+
+    submit.click(
+        app_function,
+        inputs=[user_input, location, query, chatbot],
+        outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html],
+    )
+
 app.launch()