app.py

import gradio as gr
import random
from PIL import Image

from funcs import detect_emotions, cosine_distance, generate_triggers_img, get_doc_response_emotions, summarize_and_recommend


with gr.Blocks() as demo:
    gr.Markdown("""# Falcon Cognitive Behavioural Therapy Assistant
    ## - Your Personal AI Therapist. Start chatting... Once you finish chatting, click "Generate Session Notes & Recommendations" button and wait for few seconds for them to appear""")

    therapy_session_conversation = gr.State([])

    img_paths = ['2.jpg','3.jpeg','4.jpeg','5.jpeg','6.jpeg']
    img_path = random.choice(img_paths)
    img_1 = Image.open(img_path)
    img_path = '7.jpg'
    img_2 = Image.open(img_path)

    with gr.Row():

        with gr.Column(scale=1):
            image = gr.Image(type='pil', label ='Your Personal Therapy Assistant', value=img_1, interactive=False)
            emotions = gr.Image(value=img_2, label='Top 5 Emotion Triggers')
            summary_notes = gr.Textbox(label="Summary Notes of the Session", visible=False)

        with gr.Column(scale=2):
            chatbox = gr.Chatbot(label="Therapy Session Conversation",value =[[None, 'Therapist: Hello, What can I do for you?']], height=300)
            user_input = gr.Textbox(placeholder="Enter your message here...", label="User")
            submit_button = gr.Button("Submit")
            submit_button.click(get_doc_response_emotions, [user_input, chatbox], [user_input, chatbox, emotions])
            user_input.submit(get_doc_response_emotions, [user_input, chatbox], [user_input, chatbox, emotions]) 
            recommendations = gr.Textbox(label="Recommended Actions", visible = False)

            def summarize_and_recommend_process(tcs):
              sn, r, cb = summarize_and_recommend(tcs)
              return gr.update(visible=True, value=sn), gr.update(visible=True, value=r), gr.update(visible=True, value=cb)

    process_button = gr.Button("Generate Session Notes & Recommendations")
    clear = gr.ClearButton(components=[user_input, chatbox, emotions, summary_notes, recommendations], value="Clear console")
    process_button.click(summarize_and_recommend_process, inputs=[chatbox], outputs=[summary_notes, recommendations, chatbox])


demo.launch(debug=True, share=True)