Update app.py

app.py

@@ -11,13 +11,11 @@ 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
-import random
-import json
-import pickle
-from nltk.tokenize import word_tokenize
-from nltk.stem.lancaster import LancasterStemmer
 
 # Ensure necessary NLTK resources are downloaded
 nltk.download('punkt')
@@ -39,56 +37,38 @@ try:
 except FileNotFoundError:
     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")
-except FileNotFoundError:
-    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)
-
-# 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
+# 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")
@@ -98,16 +78,15 @@ model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp
 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"
 
-# Approximate latitude and longitude for Hawaii (e.g., Oahu)
-location = "21.3,-157.8"  # Center of Hawaii (Oahu)
-radius = 50000  # 50 km radius
-
 # 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 = {
@@ -147,49 +126,6 @@ def get_place_details(place_id, api_key):
     else:
         return {}
 
-# Scrape website URL from Google Maps results (using Selenium WebDriver)
-def scrape_div_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
-
-# Scraping the website to extract phone number or email
-def scrape_div_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 all places data including pagination
 def get_all_places(query, location, radius, api_key):
     all_results = []
@@ -207,7 +143,7 @@ def get_all_places(query, location, radius, api_key):
                 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_reviews_total", "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", {})
@@ -225,7 +161,7 @@ def get_all_places(query, location, radius, api_key):
                     website = scrape_div_from_google_maps(name)
 
                 all_results.append([name, address, phone_number, rating, business_status,
-                                    user_div_total, website, types, latitude, longitude,
+                                    user_reviews_total, website, types, latitude, longitude,
                                     details.get("opening_hours", "Not available"),
                                     details.get("reviews", "Not available"), email])
 
@@ -251,6 +187,24 @@ def save_to_csv(data, filename):
         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)
@@ -266,31 +220,32 @@ with gr.Blocks() as demo:
 
     # 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):
-        pipe = pipeline("text-classification", model=model, tokenizer=tokenizer_sentiment)
-        result = pipe(text)
-        emotion = result[0]['label']
+        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
 
-    user_input_emotion.change(predict_emotion, inputs=user_input_emotion, outputs=gr.Textbox(label="Emotion Detected"))
-
-    # Provide suggestions based on the detected 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)"
+            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)"
+            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)"
+            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)"
+            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")
-    emotion_output.change(show_suggestions, inputs=emotion_output, outputs=gr.Textbox(label="Suggestions"))
+    submit_emotion.click(predict_emotion, inputs=user_input_emotion, outputs=emotion_output)
 
     # Button for summary
     def show_summary(emotion):
@@ -303,7 +258,7 @@ with gr.Blocks() as demo:
     # Chatbot functionality
     chatbot = gr.Chatbot(label="Chat")
     message_input = gr.Textbox(lines=1, label="Message")
-    history_state = gr.State([])
+    submit_chat = gr.Button("Send")
 
     def chat(message, history):
         history = history or []
@@ -330,11 +285,35 @@ with gr.Blocks() as demo:
         history.append((message, response))
         return history, history
 
-    message_input.submit(chat, inputs=[message_input, history_state], outputs=[chatbot, history_state])
+    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_navby_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")
@@ -345,7 +324,7 @@ with gr.Blocks() as demo:
         return sentiment
 
     sentiment_output = gr.Textbox(label="Predicted Sentiment")
-    user_input_sentiment.change(predict_sentiment, inputs=user_input_sentiment, outputs=sentiment_output)
+    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")