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Create app.py
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app.py
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import gradio as gr
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from app_functions import Get_DialoGPT_Response, Get_DistilGPT_Response, Get_MedGPT_Response
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# Custom CSS for styling (optional)
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custom_css = """
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body {
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background-color: #121212;
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color: #E0E0E0;
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font-family: 'Poppins', sans-serif;
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}
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.gradio-container {
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width: 70%;
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margin: 0 auto;
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padding: 20px;
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}
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h1 {
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color: #FF6347;
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font-size: 50px;
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text-align: center;
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font-weight: bold;
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}
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h3 {
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color: #FF6347;
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text-align: center;
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}
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textarea, input, select {
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background-color: #1E1E2F;
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color: #E0E0E0;
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border: 1px solid #3D3D5C;
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border-radius: 6px;
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padding: 10px;
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font-size: 14px;
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}
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button {
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background-color: #007BFF;
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color: white;
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font-size: 16px;
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font-weight: bold;
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padding: 10px;
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border-radius: 6px;
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border: none;
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cursor: pointer;
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transition: all 0.3s;
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width: 50%;
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margin: 0 auto;
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}
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button:hover {
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background-color: #0056b3;
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}
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.response-box {
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background-color: #1E1E2F;
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padding: 15px;
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border-radius: 6px;
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color: #E0E0E0;
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font-size: 16px;
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min-height: 100px;
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text-align: left;
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}
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"""
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# Build Gradio interface
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with gr.Blocks(css=custom_css) as interface:
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gr.Markdown("<h1>Healthwise AI Assistant π</h1>")
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gr.Markdown("<h3 style='color: #FF6347;'>Get accurate health-related advice tailored for your needs π</h3>")
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with gr.Row():
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with gr.Column(scale=1):
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gr.Markdown("<h3>Your Query</h3>")
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input_text = gr.Textbox(label="Question", placeholder="Type your health-related question...", lines=2)
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no_words = gr.Textbox(label="Max No. of Words", placeholder="Enter max words for the response (e.g., 100)")
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with gr.Column(scale=1):
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gr.Markdown("<h3>Preferences</h3>")
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user_type = gr.Radio(label="Answer For", choices=["Professional", "Practitioner", "General"], value="General")
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model_type = gr.Radio(label="Model Type", choices=["DialoGPT", "DistilGPT", "MedGPT"], value="DialoGPT")
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with gr.Row():
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submit_button = gr.Button("Generate Response")
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with gr.Row():
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gr.Markdown("<h3>AI Assistant's Advice π</h3>")
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with gr.Row():
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response = gr.HTML("<div class='response-box'>The AI's response will appear here...</div>")
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submit_button.click(generate_response, inputs=[input_text, no_words, user_type, model_type], outputs=response)
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interface.launch()
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