Update app.py

app.py

@@ -1,23 +1,16 @@
+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 torch
 from nltk.tokenize import word_tokenize
 from nltk.stem.lancaster import LancasterStemmer
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-import torch
 import requests
 import pandas as pd
-import time
-from bs4 import BeautifulSoup
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-import chromedriver_autoinstaller
-import os
 import tempfile
 
 # Ensure necessary NLTK resources are downloaded
@@ -90,7 +83,7 @@ def chat(message, history):
     history.append((message, response))
     return history, history
 
-# Sentiment Analysis (Code 2)
+# Sentiment Analysis
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 
@@ -102,7 +95,7 @@ def analyze_sentiment(user_input):
     sentiment = ["Negative", "Neutral", "Positive"][predicted_class]
     return f"**Predicted Sentiment:** {sentiment}"
 
-# Emotion Detection (Code 3)
+# Emotion Detection
 tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
@@ -113,21 +106,52 @@ def detect_emotion(user_input):
     return emotion
 
 def provide_suggestions(emotion):
-    suggestions = ""
+    suggestions = pd.DataFrame(columns=["Subject", "Article URL", "Video URL"])
+
     if emotion == 'joy':
-        suggestions += "You're feeling happy! Keep up the great mood!"
+        suggestions = suggestions.append({
+            "Subject": "Relaxation Techniques",
+            "Article URL": "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation",
+            "Video URL": "https://youtu.be/m1vaUGtyo-A"
+        }, ignore_index=True)
+        suggestions = suggestions.append({
+            "Subject": "Dealing with Stress",
+            "Article URL": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety",
+            "Video URL": "https://youtu.be/MIc299Flibs"
+        }, ignore_index=True)
+
     elif emotion == 'anger':
-        suggestions += "You're feeling angry. It's okay to feel this way."
+        suggestions = suggestions.append({
+            "Subject": "Managing Anger",
+            "Article URL": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety",
+            "Video URL": "https://youtu.be/MIc299Flibs"
+        }, ignore_index=True)
+
     elif emotion == 'fear':
-        suggestions += "You're feeling fearful. Take a moment to breathe."
+        suggestions = suggestions.append({
+            "Subject": "Coping with Anxiety",
+            "Article URL": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety",
+            "Video URL": "https://youtu.be/yGKKz185M5o"
+        }, ignore_index=True)
+
     elif emotion == 'sadness':
-        suggestions += "You're feeling sad. It's okay to take a break."
+        suggestions = suggestions.append({
+            "Subject": "Dealing with Sadness",
+            "Article URL": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety",
+            "Video URL": "https://youtu.be/-e-4Kx5px_I"
+        }, ignore_index=True)
+
     elif emotion == 'surprise':
-        suggestions += "You're feeling surprised. It's okay to feel neutral!"
+        suggestions = suggestions.append({
+            "Subject": "Managing Stress",
+            "Article URL": "https://www.health.harvard.edu/health-a-to-z",
+            "Video URL": "https://youtu.be/m1vaUGtyo-A"
+        }, ignore_index=True)
+        
     return suggestions
 
-# Google Places API (Code 4)
-api_key = "GOOGLE_API_KEY"  # Replace with your API key
+# Google Places API to get nearby wellness professionals
+api_key = "YOUR_GOOGLE_API_KEY"  # Replace with your API key
 
 def get_places_data(query, location, radius, api_key, next_page_token=None):
     url = "https://maps.googleapis.com/maps/api/place/textsearch/json"
@@ -162,32 +186,15 @@ def get_all_places(query, location, radius, api_key):
             break
     return all_results
 
-# Search Wellness Professionals
 def search_wellness_professionals(location):
     query = "therapist OR counselor OR mental health professional"
     radius = 50000
     google_places_data = get_all_places(query, location, radius, api_key)
-    
-    # Check if data is found
     if google_places_data:
         df = pd.DataFrame(google_places_data, columns=["Name", "Address", "Website"])
-        
-        # Create a temporary file to store the CSV
-        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
-        df.to_csv(temp_file, index=False)
-        temp_file.close()  # Close the file so that Gradio can download it
-        
-        return temp_file.name  # Return the path to the temporary file
+        return df
     else:
-        # If no data found, return a dummy CSV with a message
-        dummy_df = pd.DataFrame([["No data found.", "", ""]], columns=["Name", "Address", "Website"])
-        
-        # Create a temporary file for the dummy data
-        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
-        dummy_df.to_csv(temp_file, index=False)
-        temp_file.close()  # Close the file
-        
-        return temp_file.name  # Return the path to the dummy file
+        return pd.DataFrame([["No data found.", "", ""]], columns=["Name", "Address", "Website"])
 
 # Gradio Interface
 def gradio_interface(message, location, state):
@@ -221,7 +228,7 @@ iface = gr.Interface(
         gr.Chatbot(label="Chat History"),
         gr.Textbox(label="Sentiment Analysis"),
         gr.Textbox(label="Detected Emotion"),
-        gr.Textbox(label="Suggestions"),
+        gr.Dataframe(label="Suggestions & Resources"),
         gr.File(label="Download Wellness Professionals CSV"),
         gr.State()  # One state output
     ],