Update app.py

app.py

@@ -2,9 +2,6 @@ import gradio as gr
 import nltk
 import numpy as np
 import tflearn
-import random
-import json
-import pickle
 import torch
 from nltk.tokenize import word_tokenize
 from nltk.stem.lancaster import LancasterStemmer
@@ -17,10 +14,10 @@ from selenium import webdriver
 from selenium.webdriver.chrome.options import Options
 import chromedriver_autoinstaller
 
-# Ensure necessary NLTK resources are downloaded
+# Ensure NLTK resources are downloaded
 nltk.download('punkt')
 
-# --- Constants ---
+# Constants
 GOOGLE_MAPS_API_KEY = os.environ.get("GOOGLE_API_KEY")  # Get API key from environment variable
 if not GOOGLE_MAPS_API_KEY:
     raise ValueError("Error: GOOGLE_MAPS_API_KEY environment variable not set.")
@@ -29,8 +26,7 @@ url = "https://maps.googleapis.com/maps/api/place/textsearch/json"
 places_details_url = "https://maps.googleapis.com/maps/api/place/details/json"
 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"
 
-
-# --- Chatbot Logic ---
+# Chatbot
 stemmer = LancasterStemmer()
 
 try:
@@ -40,8 +36,8 @@ except FileNotFoundError:
     raise FileNotFoundError("Error: 'intents.json' file not found.")
 
 try:
-    with open("data.pickle", "rb") as f:
-        words, labels, training, output = pickle.load(f)
+    with open("data.pickle", "rb") as file:
+        words, labels, training, output = pickle.load(file)
 except FileNotFoundError:
     raise FileNotFoundError("Error: 'data.pickle' file not found.")
 
@@ -49,9 +45,9 @@ 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)
 
 model = tflearn.DNN(net)
+
 try:
     model.load("MentalHealthChatBotmodel.tflearn")
 except FileNotFoundError:
@@ -78,47 +74,39 @@ def chat(message, history):
             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))
     except Exception as e:
-        response = f"An error occurred: {str(e)}"
-    history.append((message, response))
+        response = "I'm sorry, I didn't understand that. Could you please rephrase?"
+        history.append((message, response))
     return history, history
 
-# --- Sentiment Analysis ---
+# Sentiment Analysis
 tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 
 def analyze_sentiment(text):
     try:
-        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+        inputs = tokenizer(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]
+            logits = model_sentiment(**inputs).logits
+        sentiment = ["Negative", "Neutral", "Positive"][torch.argmax(logits)]
         return f"**Predicted Sentiment:** {sentiment}"
     except Exception as e:
         return f"Error analyzing sentiment: {str(e)}"
 
-# --- Emotion Detection (Placeholder) ---
+# Emotion Detection
 def detect_emotion(text):
-    # Replace with your actual emotion detection logic
+    # Implement your own emotion detection logic
     return "Emotion detection not implemented"
 
-# --- Suggestion Generation (Placeholder) ---
+# Suggestion Generation
 def provide_suggestions(emotion):
-    # Replace with your actual suggestion generation logic
+    # Implement your own suggestion generation logic
     return pd.DataFrame(columns=["Subject", "Article URL", "Video URL"])
 
-# --- Google Places API Functions ---
+# Google Places API Functions
 def get_places_data(query, location, radius, api_key, next_page_token=None):
-    params = {
-        "query": query,
-        "location": location,
-        "radius": radius,
-        "key": api_key
-    }
+    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)
@@ -140,28 +128,11 @@ def get_place_details(place_id, api_key):
     else:
         return {}
 
-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("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
-
 def get_all_places(query, location, radius, api_key):
     all_results = []
     next_page_token = None
     while True:
-        data = get_places_data(query + f" in {location}", location, radius, api_key, next_page_token)
+        data = get_places_data(query, location, radius, api_key, next_page_token)
         if data:
             results = data.get('results', [])
             for place in results:
@@ -179,52 +150,22 @@ def get_all_places(query, location, radius, api_key):
             break
     return all_results
 
-# --- Gradio Interface ---
-def gradio_interface(message, location, state, btn_chat, btn_search):
+# Gradio Interface
+def gradio_interface(message, location, state):
     history = state or []
-    if len(history) == 0:
-        if btn_chat:
-            history, _ = chat(message, history)
-            sentiment = analyze_sentiment(message)
-            emotion = detect_emotion(message)
-            suggestions = provide_suggestions(emotion)
-            if location:
-                try:
-                    wellness_results = pd.DataFrame(get_all_places(query, location, 50000, GOOGLE_MAPS_API_KEY), columns=["Name", "Address", "Phone", "Website"])
-                except Exception as e:
-                    wellness_results = pd.DataFrame([["Error fetching data: " + str(e), "", "", ""]], columns=["Name", "Address", "Phone", "Website"])
-            else:
-                wellness_results = pd.DataFrame([["", "", "", ""]], columns=["Name", "Address", "Phone", "Website"])
+    if message:
+        history, _ = chat(message, history)
+        sentiment = analyze_sentiment(message)
+        emotion = detect_emotion(message)
+        suggestions = provide_suggestions(emotion)
+        if location:
+            try:
+                wellness_results = pd.DataFrame(get_all_places(query, location, 50000, GOOGLE_MAPS_API_KEY), columns=["Name", "Address", "Phone", "Website"])
+            except Exception as e:
+                wellness_results = pd.DataFrame([["Error fetching data: " + str(e), "", "", ""]], columns=["Name", "Address", "Phone", "Website"])
         else:
-            history = history
-            sentiment = ""
-            emotion = ""
-            suggestions = pd.DataFrame(columns=["Subject", "Article URL", "Video URL"])
-            wellness_results = pd.DataFrame([["", "", "", ""]], columns=["Name", "Address", "Phone", "Website"])
-    elif len(history) > 0 and location == "":
-        if btn_chat:
-            history, _ = chat(message, history)
-            sentiment = analyze_sentiment(message)
-            emotion = detect_emotion(message)
-            suggestions = provide_suggestions(emotion)
             wellness_results = pd.DataFrame([["", "", "", ""]], columns=["Name", "Address", "Phone", "Website"])
-        else:
-            history = history
-            sentiment = ""
-            emotion = ""
-            suggestions = pd.DataFrame(columns=["Subject", "Article URL", "Video URL"])
-            wellness_results = pd.DataFrame([["", "", "", ""]], columns=["Name", "Address", "Phone", "Website"])
-    elif len(history) > 0 and location != "" and btn_search:
-        try:
-            wellness_results = pd.DataFrame(get_all_places(query, location, 50000, GOOGLE_MAPS_API_KEY), columns=["Name", "Address", "Phone", "Website"])
-            sentiment = analyze_sentiment(message)
-            emotion = detect_emotion(message)
-            suggestions = provide_suggestions(emotion)
-            history, _ = chat(message, history)
-        except Exception as e:
-            wellness_results = pd.DataFrame([["Error: " + str(e), "", "", ""]], columns=["Name", "Address", "Phone", "Website"])
     else:
-        history = history
         sentiment = ""
         emotion = ""
         suggestions = pd.DataFrame(columns=["Subject", "Article URL", "Video URL"])
@@ -232,14 +173,13 @@ def gradio_interface(message, location, state, btn_chat, btn_search):
 
     return history, sentiment, emotion, suggestions, wellness_results, history
 
-iface = gr.Interface(
+gr.Interface(
     fn=gradio_interface,
     inputs=[
         gr.Textbox(label="Enter your message", placeholder="How are you feeling today?"),
         gr.Textbox(label="Enter your location (e.g., 'Hawaii, USA')", placeholder="Enter your location (optional)"),
         gr.State(),
-        gr.Button("Chat"),
-        gr.Button("Search")
+        gr.Button("Send")
     ],
     outputs=[
         gr.Chatbot(label="Chatbot Responses"),
@@ -252,6 +192,4 @@ iface = gr.Interface(
     live=True,
     title="Mental Health Chatbot with Wellness Professional Search",
     description="This chatbot provides mental health support with sentiment analysis, emotion detection, suggestions, and a list of nearby wellness professionals. Interact with the chatbot first, then enter a location to search."
-)
-
-iface.launch(debug=True, share=True)
+).launch(debug=True, share=True)