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import gradio as gr |
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import random |
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from PIL import Image |
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from funcs import detect_emotions, cosine_distance, generate_triggers_img, summarize_and_recommend |
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def submit(user_message, therapy_session_conversation, session_conversation): |
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response = get_doc_response_emotions(user_message, therapy_session_conversation) |
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return '', therapy_session_conversation, emotions_msg |
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def sum_rec(session_conversation): |
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full_summary, full_recommendations = summarize_and_recommend(session_conversation) |
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return full_summary, full_recommendations |
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with gr.Blocks() as demo: |
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img_paths = ['2.jpg','3.jpeg','4.jpeg','5.jpeg','6.jpeg'] |
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img_path = random.choice(img_paths) |
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img_1 = Image.open(img_path) |
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img_path = '7.jpg' |
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img_2 = Image.open(img_path) |
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gr.Markdown("""# Falcon Cognitive Behavioural Therapy Assistant |
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- Your Personal AI Therapist. Start chatting...""") |
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session_conversation = gr.State([]) |
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with gr.Row(): |
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with gr.Column(scale=1): |
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image = gr.Image(type='pil', label ='Your Personal Therapy Assistant', value=img_1, interactive=False) |
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emotions = gr.Image(value=img_2, label='Top 5 Emotion Triggers') |
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summary_notes = gr.Textbox(label="Summary Notes of the Session", visible=False) |
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with gr.Column(scale=2): |
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chatbox = gr.Chatbot(label="Therapy Session Conversation",value =[[None, 'Therapist: Hello, What can I do for you?']], height=300) |
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user_input = gr.Textbox(placeholder="Enter your message here...", label="User") |
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submit_button = gr.Button("Submit") |
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submit_button.click(submit, [user_input, chatbox, session_conversation], [user_input, chatbox, emotions]) |
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user_input.submit(submit, [user_input, chatbox, session_conversation], [user_input, chatbox, emotions]) |
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recommendations = gr.Textbox(label="Recommended Actions", visible = False) |
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def summarize_and_recommend_process(): |
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sn, r = summarize_and_recommend() |
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return gr.update(visible=True, value=sn), gr.update(visible=True, value=r) |
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process_button = gr.Button("Generate Session Notes & Recommendations") |
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clear = gr.ClearButton(components=[user_input, chatbox, emotions, summary_notes, recommendations], value="Clear console") |
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process_button.click(sum_rec, inputs=None, outputs=[summary_notes, recommendations]) |
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demo.launch(debug=True, share=True) |
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