Update app.py

app.py

@@ -1,348 +1,577 @@
-import streamlit as st
+import gradio as gr
+
 import nltk
+
 import numpy as np
+
 import tflearn
+
 import tensorflow
+
 import random
+
 import json
+
 import pickle
-import gradio as gr
+
+import nltk
+
 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
+
 import re
+
 from bs4 import BeautifulSoup
+
 import pandas as pd
+
 from selenium import webdriver
+
 from selenium.webdriver.chrome.options import Options
+
 import chromedriver_autoinstaller
+
 import os
+
 import torch
 
 # Ensure necessary NLTK resources are downloaded
+
 nltk.download('punkt')
 
 # Initialize the stemmer
+
 stemmer = LancasterStemmer()
 
 # Load intents.json
+
 try:
-    with open("intents.json") as file:
-        data = json.load(file)
+
+with open("intents.json") as file:
+
+data = json.load(file)
+
 except FileNotFoundError:
-    raise FileNotFoundError("Error: 'intents.json' file not found. Ensure it exists in the current directory.")
+
+raise FileNotFoundError("Error: 'intents.json' file not found. Ensure it exists in the current directory.")
 
 # Load preprocessed data from pickle
+
 try:
-    with open("data.pickle", "rb") as f:
-        words, labels, training, output = pickle.load(f)
+
+with open("data.pickle", "rb") as f:
+
+words, labels, training, output = pickle.load(f)
+
 except FileNotFoundError:
-    raise FileNotFoundError("Error: 'data.pickle' file not found. Ensure it exists and matches the model.")
+
+raise FileNotFoundError("Error: 'data.pickle' file not found. Ensure it exists and matches the model.")
 
 # Build the model structure
+
 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)
 
 # Load the trained model
+
 model = tflearn.DNN(net)
+
 try:
-    model.load("MentalHealthChatBotmodel.tflearn")
+
+model.load("MentalHealthChatBotmodel.tflearn")
+
 except FileNotFoundError:
-    raise FileNotFoundError("Error: Trained model file 'MentalHealthChatBotmodel.tflearn' not found.")
+
+raise FileNotFoundError("Error: Trained model file 'MentalHealthChatBotmodel.tflearn' not found.")
 
 # Function to process user input into a bag-of-words format
+
 def bag_of_words(s, words):
-    bag = [0 for _ in range(len(words))]
-    s_words = word_tokenize(s)
-    s_words = [stemmer.stem(word.lower()) for word in s_words if word.lower() in words]
-    for se in s_words:
-        for i, w in enumerate(words):
-            if w == se:
-                bag[i] = 1
-    return np.array(bag)
+
+bag = [0 for _ in range(len(words))]
+
+s_words = word_tokenize(s)
+
+s_words = [stemmer.stem(word.lower()) for word in s_words if word.lower() in words]
+
+for se in s_words:
+
+for i, w in enumerate(words):
+
+if w == se:
+
+bag[i] = 1
+
+return np.array(bag)
 
 # Chat function
+
 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
+
+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
 
 # 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
+
+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
+
+pipe = pipeline("text-classification", model=emotion_model, tokenizer=emotion_tokenizer)
+
+result = pipe(text)
+
+emotion = result[0]['label']
+
+return emotion
+
+# Function to provide suggestions based on emotion
+
+def provide_suggestions(emotion):
+
+if emotion == 'joy':
+
+return "You're feeling happy! Keep up the great mood!"
+
+elif emotion == 'anger':
+
+return "You're feeling angry. It's okay to feel this way. Let's try to calm down."
+
+# Add more conditions for other emotions...
+
+else:
+
+return "Sorry, no suggestions available for this emotion."
+
+# Combined function for emotion detection and suggestions
+
+def detect_emotion_and_suggest(text):
+
+emotion = detect_emotion(text)
+
+suggestions = provide_suggestions(emotion)
+
+return emotion, suggestions
 
 # 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
+
+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_\- $$$$]*', soup.get_text())
-        if phone_match:
-            phone_number = phone_match.group()
-        email_match = re.search(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', soup.get_text())
-        if email_match:
-            email = email_match.group()
-    except Exception as e:
-        print(f"Error scraping website {website}: {e}")
-    return phone_number, email
+
+phone_number = "Not available"
+
+email = "Not available"
+
+try:
+
+response = requests.get(website, timeout=5)
+
+soup = BeautifulSoup(response.content, 'html.parser')
+
+phone_match = re.search(r'$$?\+?[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 {}
+
+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 {}
 
 # 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
+
+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}")
+
+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
-st.set_page_config(page_title="Wellbeing Support System", layout="wide")
-
-# Display header
-st.title("Wellbeing Support System")
-
-# User input for location
-location = st.text_input("Enter your location:", "Hawaii")
-
-# 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="Wellbeing for All, ** I am your Best Friend **",
-    )
-    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.")
+
+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
+
+# Function to find local wellness professionals
+
+def find_wellness_professionals(location):
+
+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:
+
+df = pd.DataFrame(google_places_data, columns=[
+
+"Name", "Address", "Phone", "Rating", "Business Status",
+
+"User Ratings Total", "Website", "Types", "Latitude", "Longitude",
+
+"Opening Hours", "Reviews", "Email"
+
+])
+
+return df
+
+else:
+
+return pd.DataFrame()
+
+with gr.Blocks() as demo:
+
+gr.Markdown("# Wellbeing Support System")
+
+with gr.Tab("Chatbot"):
+
+chatbot = gr.Chatbot()
+
+msg = gr.Textbox()
+
+clear = gr.Button("Clear")
+
+msg.submit(chat, inputs=[msg, chatbot], outputs=chatbot)
+
+clear.click(lambda: None, None, chatbot)
+
+with gr.Tab("Sentiment Analysis"):
+
+text_input = gr.Textbox(label="Enter text to analyze sentiment:")
+
+analyze_button = gr.Button("Analyze Sentiment")
+
+sentiment_output = gr.Textbox(label="Sentiment:")
+
+analyze_button.click(analyze_sentiment, inputs=text_input, outputs=sentiment_output)
+
+with gr.Tab("Emotion Detection & Suggestions"):
+
+emotion_input = gr.Textbox(label="How are you feeling today?", value="Enter your thoughts here...")
+
+detect_button = gr.Button("Detect Emotion")
+
+emotion_output = gr.Textbox(label="Detected Emotion:")
+
+suggestions_output = gr.Textbox(label="Suggestions:")
+
+detect_button.click(detect_emotion_and_suggest, inputs=emotion_input, outputs=[emotion_output, suggestions_output])
+
+with gr.Tab("Find Local Wellness Professionals"):
+
+location_input = gr.Textbox(label="Enter your location:", value="Hawaii")
+
+search_button = gr.Button("Search")
+
+results_output = gr.Dataframe(label="Search Results")
+
+search_button.click(find_wellness_professionals, inputs=location_input, outputs=results_output)
+
+demo.launch()