Update app.py

app.py

@@ -1,577 +1,224 @@
 import gradio as gr
-
 import nltk
-
 import numpy as np
-
 import tflearn
-
-import tensorflow
-
 import random
-
 import json
-
 import pickle
-
-import nltk
-
+import torch
 from nltk.tokenize import word_tokenize
-
 from nltk.stem.lancaster import LancasterStemmer
-
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-from transformers import pipeline
-
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 import requests
-
-import csv
-
-import time
-
 import re
-
 from bs4 import BeautifulSoup
-
+import time
 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
+def chat(message, history, state):
+    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?"
+
+        history.append((message, response))
+
+        # Update state to move to the next feature
+        state['step'] = 2  # Move to sentiment analysis
+    except Exception as e:
+        response = f"An error occurred: {str(e)}"
+
+    return history, history, state
 
 # 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
+# Function for sentiment analysis
+def analyze_sentiment(text, state):
+    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]
+    
+    # Update state to move to the next feature
+    state['step'] = 3  # Move to emotion detection and suggestions
+    return sentiment, state
 
+# 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 provide suggestions based on emotion
-
+def detect_emotion(text, state):
+    pipe = pipeline("text-classification", model=emotion_model, tokenizer=emotion_tokenizer)
+    result = pipe(text)
+    emotion = result[0]['label']
+
+    # Provide suggestions based on emotion
+    suggestions = provide_suggestions(emotion)
+    
+    # Update state to move to the next feature
+    state['step'] = 4  # Move to wellness professional search
+    return emotion, suggestions, state
+
+# Suggestions based on detected 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
-
-# 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 {}
-
-# Function to get all places data including pagination
-
+    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."
+    elif emotion == 'fear':
+        return "You're feeling fearful. Take deep breaths, everything will be okay."
+    elif emotion == 'sadness':
+        return "You're feeling sad. It's okay, things will get better. You're not alone."
+    else:
+        return "Sorry, no suggestions available for this emotion."
+
+# Function to find wellness professionals
+def find_wellness_professionals(location, state):
+    query = "therapist OR counselor OR mental health professional OR marriage and family therapist OR psychotherapist OR psychiatrist OR psychologist in " + location
+    api_key = "YOUR_GOOGLE_API_KEY"  # Replace with your own API key
+    location_coords = "21.3,-157.8"  # Default to Oahu, Hawaii
+    radius = 50000  # 50 km radius
+    
+    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, state
+    else:
+        return pd.DataFrame(), state
+
+# The functions for scraping websites and fetching details
 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
-
-# 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()
-
+    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")
+                email = details.get("email", "Not available")
+                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)
+    return all_results
+
+# Gradio interface
 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()
+    gr.Markdown("# Wellbeing Support System")
+    
+    state = gr.State({"step": 1})  # Track the flow step
+
+    with gr.Tab("Chatbot"):
+        chatbot = gr.Chatbot()
+        msg = gr.Textbox()
+        clear = gr.Button("Clear")
+        msg.submit(chat, inputs=[msg, chatbot, state], outputs=[chatbot, chatbot, state])
+        clear.click(lambda: None, None, chatbot)
+
+    with gr.Tab("Sentiment Analysis"):
+        sentiment_output = gr.Textbox(label="Sentiment:")
+        text_input = gr.Textbox(label="Enter text to analyze sentiment:")
+        analyze_button = gr.Button("Analyze Sentiment")
+        analyze_button.click(analyze_sentiment, inputs=[text_input, state], outputs=[sentiment_output, state])
+
+    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, inputs=[emotion_input, state], outputs=[emotion_output, suggestions_output, state])
+
+    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, state], outputs=[results_output, state])
+
+demo.launch()