Update app.py

app.py

@@ -1,3 +1,4 @@
+import streamlit as st
 import nltk
 import numpy as np
 import tflearn
@@ -8,6 +9,8 @@ import pickle
 import gradio as gr
 from nltk.tokenize import word_tokenize
 from nltk.stem.lancaster import LancasterStemmer
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
 import requests
 import csv
 import time
@@ -18,7 +21,6 @@ from selenium import webdriver
 from selenium.webdriver.chrome.options import Options
 import chromedriver_autoinstaller
 import os
-import logging
 
 # Ensure necessary NLTK resources are downloaded
 nltk.download('punkt')
@@ -91,153 +93,255 @@ def chat(message, history):
     history.append((message, response))
     return history, history
 
-# Load the pre-trained model (cached for performance)
-def load_model():
-    return pipeline('sentiment-analysis', model='cardiffnlp/twitter-roberta-base-sentiment')
-
-sentiment_model = load_model()
-
-# Define the function to analyze sentiment
-def analyze_sentiment(user_input):
-    result = sentiment_model(user_input)[0]
-    sentiment = result['label'].lower()  # Convert to lowercase for easier comparison
-    
-    # Customize messages based on detected sentiment
-    if sentiment == 'negative':
-        return "Mood Detected: Negative 😔\n\nStay positive! 🌟 Remember, tough times don't last, but tough people do!"
-    elif sentiment == 'neutral':
-        return "Mood Detected: Neutral 😐\n\nIt's good to reflect on steady days. Keep your goals in mind, and stay motivated!"
-    elif sentiment == 'positive':
-        return "Mood Detected: Positive 😊\n\nYou're on the right track! Keep shining! 🌞"
+# Load pre-trained model and tokenizer for sentiment analysis
+tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+sentiment_model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+
+# Load pre-trained model and tokenizer for emotion detection
+emotion_tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+emotion_model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+
+# Function for sentiment analysis
+def analyze_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = sentiment_model(**inputs)
+    predicted_class = torch.argmax(outputs.logits, dim=1).item()
+    sentiment = ["Negative", "Neutral", "Positive"][predicted_class]
+    return sentiment
+
+# Function for emotion detection
+def detect_emotion(text):
+    pipe = pipeline("text-classification", model=emotion_model, tokenizer=emotion_tokenizer)
+    result = pipe(text)
+    emotion = result[0]['label']
+    return emotion
+
+# Function to scrape website URL from Google Maps using Selenium
+def scrape_website_from_google_maps(place_name):
+    chrome_options = Options()
+    chrome_options.add_argument("--headless")
+    chrome_options.add_argument("--no-sandbox")
+    chrome_options.add_argument("--disable-dev-shm-usage")
+    driver = webdriver.Chrome(options=chrome_options)
+    search_url = f"https://www.google.com/maps/search/{place_name.replace(' ', '+')}"
+    driver.get(search_url)
+    time.sleep(5)
+    try:
+        website_element = driver.find_element_by_xpath('//a[contains(@aria-label, "Visit") and contains(@aria-label, "website")]')
+        website_url = website_element.get_attribute('href')
+    except:
+        website_url = "Not available"
+    driver.quit()
+    return website_url
+
+# Function to scrape website for contact information
+def scrape_website_for_contact_info(website):
+    phone_number = "Not available"
+    email = "Not available"
+    try:
+        response = requests.get(website, timeout=5)
+        soup = BeautifulSoup(response.content, 'html.parser')
+        phone_match = re.search(r'$$?\+?[0-9]*$$?[0-9_\- $$$$]*', soup.get_text())
+        if phone_match:
+            phone_number = phone_match.group()
+        email_match = re.search(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', soup.get_text())
+        if email_match:
+            email = email_match.group()
+    except Exception as e:
+        print(f"Error scraping website {website}: {e}")
+    return phone_number, email
+
+# Function to fetch detailed information for a specific place using its place_id
+def get_place_details(place_id, api_key):
+    details_url = "https://maps.googleapis.com/maps/api/place/details/json"
+    params = {
+        "place_id": place_id,
+        "key": api_key
+    }
+    response = requests.get(details_url, params=params)
+    if response.status_code == 200:
+        details_data = response.json().get("result", {})
+        return {
+            "opening_hours": details_data.get("opening_hours", {}).get("weekday_text", "Not available"),
+            "reviews": details_data.get("reviews", "Not available"),
+            "phone_number": details_data.get("formatted_phone_number", "Not available"),
+            "website": details_data.get("website", "Not available")
+        }
     else:
-        return "Mood Detected: Unknown 🤔\n\nKeep going, you're doing great!"
-
-# Load pre-trained model and tokenizer
-@st.cache_resource
-def load_model():
-    tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
-    model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
-    return tokenizer, model
-
-tokenizer, model = load_model()
+        return {}
+
+# Function to get all places data including pagination
+def get_all_places(query, location, radius, api_key):
+    all_results = []
+    next_page_token = None
+    while True:
+        data = get_places_data(query, location, radius, api_key, next_page_token)
+        if data:
+            results = data.get('results', [])
+            for place in results:
+                place_id = place.get("place_id")
+                name = place.get("name")
+                address = place.get("formatted_address")
+                rating = place.get("rating", "Not available")
+                business_status = place.get("business_status", "Not available")
+                user_ratings_total = place.get("user_ratings_total", "Not available")
+                website = place.get("website", "Not available")
+                types = ", ".join(place.get("types", []))
+                location = place.get("geometry", {}).get("location", {})
+                latitude = location.get("lat", "Not available")
+                longitude = location.get("lng", "Not available")
+                details = get_place_details(place_id, api_key)
+                phone_number = details.get("phone_number", "Not available")
+                if phone_number == "Not available" and website != "Not available":
+                    phone_number, email = scrape_website_for_contact_info(website)
+                else:
+                    email = "Not available"
+                if website == "Not available":
+                    website = scrape_website_from_google_maps(name)
+                all_results.append([name, address, phone_number, rating, business_status,
+                                    user_ratings_total, website, types, latitude, longitude,
+                                    details.get("opening_hours", "Not available"),
+                                    details.get("reviews", "Not available"), email
+                                    ])
+            next_page_token = data.get('next_page_token')
+            if not next_page_token:
+                break
+            time.sleep(2)
+        else:
+            break
+    return all_results
+
+# Function to save results to CSV file
+def save_to_csv(data, filename):
+    with open(filename, mode='w', newline='', encoding='utf-8') as file:
+        writer = csv.writer(file)
+        writer.writerow([
+            "Name", "Address", "Phone", "Rating", "Business Status",
+            "User Ratings Total", "Website", "Types", "Latitude", "Longitude",
+            "Opening Hours", "Reviews", "Email"
+        ])
+        writer.writerows(data)
+    print(f"Data saved to {filename}")
+
+# Function to get places data from Google Places API
+def get_places_data(query, location, radius, api_key, next_page_token=None):
+    url = "https://maps.googleapis.com/maps/api/place/textsearch/json"
+    params = {
+        "query": query,
+        "location": location,
+        "radius": radius,
+        "key": api_key
+    }
+    if next_page_token:
+        params["pagetoken"] = next_page_token
+    response = requests.get(url, params=params)
+    if response.status_code == 200:
+        data = response.json()
+        return data
+    else:
+        print(f"Error: {response.status_code} - {response.text}")
+        return None
 
-# Set page config as the very first Streamlit command
-st.set_page_config(page_title="Mental Health & Wellness Assistant", layout="wide")
+# Set page config
+st.set_page_config(page_title="Wellbeing Support System", layout="wide")
 
 # Display header
-st.title("Mental Health & Wellness Assistant")
-
-# User input for text (emotion detection)
-user_input = st.text_area("How are you feeling today?", "Enter your thoughts here...")
-
-# Model prediction
-if user_input:
-    pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
-    result = pipe(user_input)
+st.title("Wellbeing Support System")
 
-    # Extracting the emotion from the model's result
-    emotion = result[0]['label']
+# User input for location
+location = st.text_input("Enter your location:", "Hawaii")
 
-    # Display emotion
-    st.write(f"**Emotion Detected:** {emotion}")
-    
-    # Provide suggestions based on the detected emotion
-    if emotion == 'joy':
-        st.write("You're feeling happy! Keep up the great mood!")
-        st.write("Useful Resources:")
-        st.markdown("[Relaxation Techniques](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)")
-        st.write("[Dealing with Stress](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
-        st.write("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
-        
-        st.write("Relaxation Videos:")
-        st.markdown("[Watch on YouTube](https://youtu.be/m1vaUGtyo-A)")
-
-    elif emotion == 'anger':
-        st.write("You're feeling angry. It's okay to feel this way. Let's try to calm down.")
-        st.write("Useful Resources:")
-        st.markdown("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
-        st.write("[Stress Management Tips](https://www.health.harvard.edu/health-a-to-z)")
-        st.write("[Dealing with Anger](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
-        
-        st.write("Relaxation Videos:")
-        st.markdown("[Watch on YouTube](https://youtu.be/MIc299Flibs)")
-
-    elif emotion == 'fear':
-        st.write("You're feeling fearful. Take a moment to breathe and relax.")
-        st.write("Useful Resources:")
-        st.markdown("[Mindfulness Practices](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)")
-        st.write("[Coping with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
-        st.write("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
-        
-        st.write("Relaxation Videos:")
-        st.markdown("[Watch on YouTube](https://youtu.be/yGKKz185M5o)")
-
-    elif emotion == 'sadness':
-        st.write("You're feeling sad. It's okay to take a break.")
-        st.write("Useful Resources:")
-        st.markdown("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
-        st.write("[Dealing with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
-        
-        st.write("Relaxation Videos:")
-        st.markdown("[Watch on YouTube](https://youtu.be/-e-4Kx5px_I)")
-
-    elif emotion == 'surprise':
-        st.write("You're feeling surprised. It's okay to feel neutral!")
-        st.write("Useful Resources:")
-        st.markdown("[Managing Stress](https://www.health.harvard.edu/health-a-to-z)")
-        st.write("[Coping Strategies](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
-        
-        st.write("Relaxation Videos:")
-        st.markdown("[Watch on YouTube](https://youtu.be/m1vaUGtyo-A)")
-
-# Chatbot functionality
-def chatbot_interface():
-    def chat(message, history):
-        history = history or []
-        message = message.lower()
-        
-        try:
-            # Predict the tag
-            results = model.predict([bag_of_words(message, words)])
-            results_index = np.argmax(results)
-            tag = labels[results_index]
-
-            # Match tag with intent and choose a random response
-            for tg in data["intents"]:
-                if tg['tag'] == tag:
-                    responses = tg['responses']
-                    response = random.choice(responses)
-                    break
-            else:
-                response = "I'm sorry, I didn't understand that. Could you please rephrase?"
-
-        except Exception as e:
-            response = f"An error occurred: {str(e)}"
-        
-        history.append((message, response))
-        return history, history
+# Tabs for different functionalities
+tabs = ["Chatbot", "Sentiment Analysis", "Emotion Detection & Suggestions", "Find Local Wellness Professionals"]
+selected_tab = st.selectbox("Select a functionality:", tabs)
 
+if selected_tab == "Chatbot":
+    # Chatbot functionality
+    st.subheader("Chat with the Mental Health Support Bot")
     chatbot = gr.Chatbot(label="Chat")
     demo = gr.Interface(
         chat,
         [gr.Textbox(lines=1, label="Message"), "state"],
         [chatbot, "state"],
         allow_flagging="never",
-        title="Mental Health Chatbot",
-        description="Your personal mental health assistant.",
+        title="Wellbeing for All, ** I am your Best Friend **",
     )
-    return demo
-
-# Launch the interfaces
-if __name__ == "__main__":
-    # Create a tabbed interface for different features
-    tabs = [
-        gr.TabItem("Sentiment Analysis", chatbot_ui()),
-        gr.TabItem("Emotion Detection", chatbot_ui()),
-        gr.TabItem("Google Places Search", chatbot_ui()),
-    ]
-
-    with gr.Blocks() as demo:
-        gr.Tabs(tabs)
-
-    demo.launch()
+    demo.launch()
+
+elif selected_tab == "Sentiment Analysis":
+    # Sentiment Analysis
+    st.subheader("Sentiment Analysis")
+    user_input = st.text_area("Enter text to analyze sentiment:")
+    if st.button("Analyze Sentiment"):
+        if user_input:
+            sentiment = analyze_sentiment(user_input)
+            st.write(f"**Sentiment:** {sentiment}")
+        else:
+            st.warning("Please enter some text to analyze.")
+
+elif selected_tab == "Emotion Detection & Suggestions":
+    # Emotion Detection and Suggestions
+    st.subheader("Emotion Detection and Well-Being Suggestions")
+    user_input = st.text_area("How are you feeling today?", "Enter your thoughts here...")
+    if st.button("Detect Emotion"):
+        if user_input:
+            emotion = detect_emotion(user_input)
+            st.write(f"**Emotion Detected:** {emotion}")
+            # Provide suggestions based on the detected emotion
+            if emotion == 'joy':
+                st.write("You're feeling happy! Keep up the great mood!")
+                st.write("Useful Resources:")
+                st.markdown("[Relaxation Techniques](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)")
+                st.write("[Dealing with Stress](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
+                st.write("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
+                st.write("Relaxation Videos:")
+                st.markdown("[Watch on YouTube](https://youtu.be/m1vaUGtyo-A)")
+            elif emotion == 'anger':
+                st.write("You're feeling angry. It's okay to feel this way. Let's try to calm down.")
+                st.write("Useful Resources:")
+                st.markdown("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
+                st.write("[Stress Management Tips](https://www.health.harvard.edu/health-a-to-z)")
+                st.write("[Dealing with Anger](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
+                st.write("Relaxation Videos:")
+                st.markdown("[Watch on YouTube](https://youtu.be/MIc299Flibs)")
+            # Add more conditions for other emotions...
+        else:
+            st.warning("Please enter some text to analyze.")
+
+elif selected_tab == "Find Local Wellness Professionals":
+    # Find Local Wellness Professionals
+    st.subheader("Find Local Wellness Professionals")
+    if st.button("Search"):
+        # Define search parameters
+        query = "therapist OR counselor OR mental health professional OR marriage and family therapist OR psychotherapist OR psychiatrist OR psychologist OR nutritionist OR wellness doctor OR holistic practitioner OR integrative medicine OR chiropractor OR naturopath in " + location
+        api_key = "AIzaSyCcfJzMFfuv_1LN7JPTJJYw_aS0A_SLeW0"  # Replace with your own Google API key
+        location_coords = "21.3,-157.8"  # Default to Oahu, Hawaii
+        radius = 50000  # 50 km radius
+
+        # Install Chrome and Chromedriver
+        def install_chrome_and_driver():
+            os.system("apt-get update")
+            os.system("apt-get install -y wget curl")
+            os.system("wget -q https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb")
+            os.system("dpkg -i google-chrome-stable_current_amd64.deb")
+            os.system("apt-get install -y -f")
+            os.system("google-chrome-stable --version")
+            chromedriver_autoinstaller.install()
+
+        install_chrome_and_driver()
+
+        # Get all places data
+        google_places_data = get_all_places(query, location_coords, radius, api_key)
+        if google_places_data:
+            # Display the results
+            df = pd.DataFrame(google_places_data, columns=[
+                "Name", "Address", "Phone", "Rating", "Business Status",
+                "User Ratings Total", "Website", "Types", "Latitude", "Longitude",
+                "Opening Hours", "Reviews", "Email"
+            ])
+            st.write(df)
+            # Save to CSV
+            save_to_csv(google_places_data, "wellness_professionals.csv")
+        else:
+            st.write("No data found.")