Update app.py

app.py

@@ -1,5 +1,4 @@
 import os
-import gradio as gr
 import nltk
 import numpy as np
 import tflearn
@@ -12,6 +11,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipe
 import googlemaps
 import folium
 import torch
+import streamlit as st
 
 # Suppress TensorFlow warnings
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
@@ -155,7 +155,7 @@ def get_health_professionals_and_map(location, query):
                 # Use a list of values to append each professional
                 professionals.append([place['name'], place.get('vicinity', 'No address provided')])
                 folium.Marker(
-                    location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
+                    location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]], 
                     popup=f"{place['name']}"
                 ).add_to(map_)
             return professionals, map_._repr_html_()
@@ -164,123 +164,46 @@ def get_health_professionals_and_map(location, query):
     except Exception as e:
         return [], ""  # Return empty list on exception
 
-# Main Application Logic
-def app_function(user_input, location, query, history):
-    chatbot_history, _ = generate_chatbot_response(user_input, history)
+# Streamlit App Layout
+st.title("🌟 Well-Being Companion")
+
+# Input fields
+user_input = st.text_input("Please Enter Your Message Here")
+location = st.text_input("Please Enter Your Current Location Here")
+query = st.text_input("Please Enter Which Health Professional You Want To Search Nearby")
+
+# Button to submit
+if st.button("Submit"):
+    chatbot_history, _ = generate_chatbot_response(user_input, [])
     sentiment_result = analyze_sentiment(user_input)
     emotion_result, cleaned_emotion = detect_emotion(user_input)
     suggestions = generate_suggestions(cleaned_emotion)
     professionals, map_html = get_health_professionals_and_map(location, query)
-    return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html
-
-# CSS Styling
-custom_css = """
-body {
-    font-family: 'Roboto', sans-serif;
-    background-color: #3c6487; /* Set the background color */
-    color: white;
-}
-
-h1 {
-    background: #ffffff;
-    color: #000000;
-    border-radius: 8px;
-    padding: 10px;
-    font-weight: bold;
-    text-align: center;
-    font-size: 2.5rem;
-}
-
-textarea, input {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-    padding: 8px;
-    font-size: 1rem;
-    caret-color: black;
-    outline: none;
-    border-radius: 8px;
-}
-
-textarea:focus, input:focus {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-    outline: none;
-}
-
-textarea:hover, input:hover {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-}
-
-.df-container {
-    background: white;
-    color: black;
-    border: 2px solid orange;
-    border-radius: 10px;
-    padding: 10px;
-    font-size: 14px;
-    max-height: 400px;
-    height: auto;
-    overflow-y: auto;
-}
-
-#suggestions-title {
-    text-align: center !important; /* Ensure the centering is applied */
-    font-weight: bold !important; /* Ensure bold is applied */
-    color: white !important; /* Ensure color is applied */
-    font-size: 4.2rem !important; /* Ensure font size is applied */
-    margin-bottom: 20px !important; /* Ensure margin is applied */
-}
-
-/* Style for the submit button */
-.gr-button {
-    background-color: #ae1c93;  /* Set the background color to #ae1c93 */
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1), 0 2px 4px rgba(0, 0, 0, 0.06);
-    transition: background-color 0.3s ease;
-}
-
-.gr-button:hover {
-    background-color: #8f167b;
-}
-
-.gr-button:active {
-    background-color: #7f156b;
-}
-"""
-# Gradio Application
-
-with gr.Blocks(css=custom_css) as app:
-    gr.HTML("<h1>🌟 Well-Being Companion</h1>")
-    with gr.Row():
-        user_input = gr.Textbox(label="Please Enter Your Message Here")
-        location = gr.Textbox(label="Please Enter Your Current Location Here")
-        query = gr.Textbox(label="Please Enter Which Health Professional You Want To Search Nearby")
-
-    # Predict Disease Button
-    predict_disease = gr.Button(value="Predict Disease", variant="secondary")
-    predict_disease.click(lambda: None, [], _js=f"() => window.open('https://huggingface.co/spaces/Mishal23/wellBeing', '_blank')")
 
-    submit = gr.Button(value="Submit", variant="primary")
+    # Display chatbot conversation history
+    st.subheader("Chat History")
+    for message, response in chatbot_history:
+        st.write(f"**You:** {message}")
+        st.write(f"**Bot:** {response}")
 
-    chatbot = gr.Chatbot(label="Chat History")
-    sentiment = gr.Textbox(label="Detected Sentiment")
-    emotion = gr.Textbox(label="Detected Emotion")
+    # Display sentiment
+    st.subheader("Detected Sentiment")
+    st.write(sentiment_result)
 
-    # Adding Suggestions Title with Styled Markdown (Centered and Bold)
-    gr.Markdown("Suggestions", elem_id="suggestions-title")
+    # Display emotion
+    st.subheader("Detected Emotion")
+    st.write(emotion_result)
 
-    suggestions = gr.DataFrame(headers=["Title", "Link"])  # Table for suggestions
-    professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])  # Changed to DataFrame
-    map_html = gr.HTML(label="Interactive Map")
+    # Display suggestions
+    st.subheader("Suggestions")
+    for suggestion, link in suggestions:
+        st.write(f"[{suggestion}]({link})")
 
-    submit.click(
-        app_function,
-        inputs=[user_input, location, query, chatbot],
-        outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html],
-    )
+    # Display professionals
+    st.subheader("Nearby Health Professionals")
+    st.write(professionals)
 
-app.launch()
+    # Display map
+    st.subheader("Interactive Map")
+    st.components.v1.html(map_html, height=500)