Update app.py

app.py

@@ -7,9 +7,7 @@ import tflearn
 import gradio as gr
 import requests
 import torch
-import pandas as pd
 import folium
-from bs4 import BeautifulSoup
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 from nltk.tokenize import word_tokenize
 from nltk.stem.lancaster import LancasterStemmer
@@ -203,12 +201,10 @@ def generate_map(wellness_data):
             icon=folium.Icon(color='blue', icon='info-sign')
         ).add_to(m)
 
-    # Save map as an HTML file
-    map_file = "wellness_map.html"
-    m.save(map_file)
+    # Save map as an HTML string
+    map_html = m._repr_html_()
     
-    # Return the HTML file path to be embedded in Gradio
-    return map_file
+    return map_html
 
 # Gradio interface setup for user interaction
 def user_interface(message, location, history, api_key, words, labels, model):
@@ -226,13 +222,14 @@ def user_interface(message, location, history, api_key, words, labels, model):
     wellness_data = get_wellness_professionals(location, api_key)
     
     # Generate the map
-    map_file = generate_map(wellness_data)
+    map_html = generate_map(wellness_data)
     
     # Create a DataFrame for the suggestions
     suggestions_df = pd.DataFrame(resources, columns=["Subject", "Article URL"])
     suggestions_df["Video URL"] = video_link  # Add video URL column
+    suggestions_html = suggestions_df.to_html(escape=False)
     
-    return history, history, sentiment, emotion, suggestions_df.to_html(escape=False), map_file
+    return history, history, sentiment, emotion, suggestions_html, map_html
 
 # Load data and model
 try:
@@ -257,7 +254,7 @@ location_input = gr.Textbox(label="Enter your location (latitude,longitude)", pl
 
 # Gradio interface definition
 demo = gr.Interface(
-    fn=lambda message, location, history: user_interface(message, location, history, GOOGLE_API_KEY, words, labels, model),
+    fn=lambda message, location, history: user_interface(message, location, history, os.getenv("GOOGLE_API_KEY"), words, labels, model),
     inputs=[
         gr.Textbox(label="Message"),
         location_input,