import gradio as gr 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 import chromedriver_autoinstaller import os import nltk import numpy as np import torch import torch.nn as nn import torch.optim as optim import tflearn import tensorflow as tf # Ensure necessary NLTK resources are downloaded nltk.download('punkt') # Import LancasterStemmer from nltk.stem from nltk.stem import LancasterStemmer # Initialize the stemmer stemmer = LancasterStemmer() # Load intents.json try: 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.") # Load preprocessed data from pickle try: 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.") # Define a PyTorch model with the same architecture as your tflearn model class PyTorchModel(nn.Module): def __init__(self, vocab_size, embedding_dim, hidden_dim, num_classes): super(PyTorchModel, self).__init__() self.embedding = nn.Embedding(vocab_size, embedding_dim) self.fc1 = nn.Linear(embedding_dim, hidden_dim) self.relu = nn.ReLU() self.fc2 = nn.Linear(hidden_dim, num_classes) def forward(self, x): out = self.embedding(x) out = torch.mean(out, dim=1) out = self.fc1(out) out = self.relu(out) out = self.fc2(out) return out # Convert the tflearn model to a PyTorch model vocab_size = len(words) embedding_dim = 128 hidden_dim = 64 num_classes = len(labels) pytorch_model = PyTorchModel(vocab_size, embedding_dim, hidden_dim, num_classes) # Load weights from the tflearn model for layer_name, weights in zip(['fc1/kernel:0', 'fc1/bias:0', 'fc2/kernel:0', 'fc2/bias:0'], model.get_weights()): pytorch_layer_name = layer_name.replace(':0', '') pytorch_model.state_dict()[pytorch_layer_name].copy_(torch.tensor(weights)) # Move the model to the CPU pytorch_model.cpu() # Load tokenizer and model for sentiment analysis tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") # Google Places API endpoint url = "https://maps.googleapis.com/maps/api/place/textsearch/json" places_details_url = "https://maps.googleapis.com/maps/api/place/details/json" # Google Geocoding API endpoint geocoding_url = "https://maps.googleapis.com/maps/api/geocode/json" # Your actual Google API Key (replace with your key) api_key = "AIzaSyCcfJzMFfuv_1LN7JPTJJYw_aS0A_SLeW0" # Replace with your own Google API key # Search query for wellness professionals in Hawaii 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 Hawaii" # Function to send a request to Google Places API and fetch places data def get_places_data(query, location, radius, api_key, next_page_token=None): 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: return response.json() else: return None # Function to fetch detailed information for a specific place using its place_id def get_place_details(place_id, api_key): details_url = places_details_url 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 fetch 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', []) if not results: break 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_reviews_total = place.get("user_reviews_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_div_for_contact_info(website) else: email = "Not available" if website == "Not available": website = scrape_div_from_google_maps(name) all_results.append([name, address, phone_number, rating, business_status, user_reviews_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 Reviews Total", "Website", "Types", "Latitude", "Longitude", "Opening Hours", "Reviews", "Email" ]) writer.writerows(data) print(f"Data saved to {filename}") # Geocoding function to convert location text to coordinates def geocode_location(address): params = { "address": address, "key": api_key } response = requests.get(geocoding_url, params=params) if response.status_code == 200: data = response.json() if data['status'] == 'OK': location = data['results'][0]['geometry']['location'] return location['lat'], location['lng'] else: raise ValueError("Geocoding failed.") else: raise ValueError("Failed to retrieve geocoding data.") # Main function to execute script def main(): google_places_data = get_all_places(query, location, radius, api_key) if google_places_data: save_to_csv(google_places_data, "wellness_professionals_hawaii.csv") else: print("No data found.") # Gradio UI setup with gr.Blocks() as demo: # Display header gr.Markdown("# Emotion Detection and Well-Being Suggestions") # User input for text (emotion detection) user_input_emotion = gr.Textbox(lines=1, label="How are you feeling today?") submit_emotion = gr.Button("Submit") # Model prediction for emotion detection def predict_emotion(text): inputs = tokenizer_sentiment(text, return_tensors="pt").to('cpu') with torch.no_grad(): outputs = pytorch_model(inputs['input_ids']) _, predicted_class = torch.max(outputs, dim=1) emotion = labels[predicted_class.item()] return emotion # Show suggestions based on the detected emotion def show_suggestions(emotion): if emotion == 'joy': return "You're feeling happy! Keep up the great mood!\nUseful Resources:\n[Relaxation Techniques](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)\n[Dealing with Stress](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)\n[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)\n\nRelaxation Videos:\n[Watch on YouTube](https://youtu.be/m1vaUGtyo-A)" elif emotion == 'anger': return "You're feeling angry. It's okay to feel this way. Let's try to calm down.\nUseful Resources:\n[Emotional wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)\n[Stress Management Tips](https://www.health.harvard.edu/health-a-to-z)\n[Dealing with Anger](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)\n\nRelaxation Videos:\n[Watch on YouTube](https://youtu.be/MIc299Flibs)" elif emotion == 'fear': return "You're feeling fearful. Take a moment to breathe and relax.\nUseful Resources:\n[Mindfulness Practices](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)\n[Coping with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)\n[Emotional wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)\n\nRelaxation Videos:\n[Watch on YouTube](https://youtu.be/yGKKz185M5o)" elif emotion == 'sadness': return "You're feeling sad. It's okay to take a break.\nUseful Resources:\n[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)\n[Dealing with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)\n\nRelaxation Videos:\n[Watch on YouTube](https://youtu.be/-e-4Kx5px_I)" elif emotion == 'surprise': return "You're feeling surprised. It's okay to feel neutral!\nUseful Resources:\n[Managing Stress](https://www.health.harvard.edu/health-a-to-z)\n[Coping Strategies](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)\n\nRelaxation Videos:\n[Watch on YouTube](https://youtu.be/m1vaUGtyo-A)" emotion_output = gr.Textbox(label="Emotion Detected") submit_emotion.click(predict_emotion, inputs=user_input_emotion, outputs=emotion_output) # Button for summary def show_summary(emotion): return f"Emotion Detected: {emotion}\nUseful Resources based on your mood:\n{show_suggestions(emotion)}" summary_button = gr.Button("Show Summary") summary_output = gr.Textbox(label="Summary") summary_button.click(show_summary, inputs=emotion_output, outputs=summary_output) # Chatbot functionality chatbot = gr.Chatbot(label="Chat") message_input = gr.Textbox(lines=1, label="Message") submit_chat = gr.Button("Send") 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 submit_chat.click(chat, inputs=[message_input, gr.State()], outputs=[chatbot, gr.State()]) # Location input for fetching nearby health professionals location_input = gr.Textbox(lines=1, label="Enter your location (plain English):") submit_location = gr.Button("Find Nearby Health Professionals") # Fetch and display nearby health professionals def fetch_nearby_health_professionals(location): try: lat, lon = geocode_location(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" radius = 50000 # 50 km radius all_results = get_all_places(query, f"{lat},{lon}", radius, api_key) if all_results: df = pd.DataFrame(all_results, columns=["Name", "Address", "Phone", "Rating", "Business Status", "User Reviews Total", "Website", "Types", "Latitude", "Longitude", "Opening Hours", "Reviews", "Email"]) return df else: return "No data found." except Exception as e: return str(e) nearby_health_professionals_table = gr.Dataframe(headers=["Name", "Address", "Phone", "Rating", "Business Status", "User Reviews Total", "Website", "Types", "Latitude", "Longitude", "Opening Hours", "Reviews", "Email"]) submit_location.click(fetch_nearby_health_professionals, inputs=location_input, outputs=nearby_health_professionals_table) # User input for text (sentiment analysis) user_input_sentiment = gr.Textbox(lines=1, label="Enter text to analyze sentiment:") submit_sentiment = gr.Button("Submit") # Prediction button for sentiment analysis def predict_sentiment(text): inputs = tokenizer_sentiment(text, return_tensors="pt") with torch.no_grad(): outputs = model_sentiment(**inputs) predicted_class = torch.argmax(outputs.logits, dim=1).item() sentiment = ["Negative", "Neutral", "Positive"][predicted_class] return sentiment sentiment_output = gr.Textbox(label="Predicted Sentiment") submit_sentiment.click(predict_sentiment, inputs=user_input_sentiment, outputs=sentiment_output) # Button to fetch wellness professionals data fetch_button = gr.Button("Fetch Wellness professionals data") data_output = gr.Dataframe(headers=["Name", "Address", "Phone", "Rating", "Business Status", "User Reviews Total", "Website", "Types", "Latitude", "Longitude", "Opening Hours", "Reviews", "Email"]) def fetch_data(): all_results = get_all_places(query, location, radius, api_key) if all_results: return pd.DataFrame(all_results, columns=["Name", "Address", "Phone", "Rating", "Business Status", "User Reviews Total", "Website", "Types", "Latitude", "Longitude", "Opening Hours", "Reviews", "Email"]) else: return "No data found." fetch_button.click(fetch_data, inputs=None, outputs=data_output) # Launch Gradio interface demo.launch()