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Update app.py
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app.py
CHANGED
@@ -4,7 +4,7 @@ import time
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import random
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# Load the model and tokenizer
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model_id = "microsoft/phi-2"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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@@ -33,114 +33,23 @@ def analyze_text(text):
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# --- Interface ---
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css = """
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.gradio-container {
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background-color: #000000; /* Black background */
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font-family: 'Roboto', sans-serif;
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}
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.gradio-interface {
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background-color: rgba(0, 0, 0, 0.8); /* Dark translucent background */
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border: 3px solid #00FF00; /* Green border */
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padding: 20px;
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box-shadow: 0 0 10px rgba(0, 255, 0, 0.5); /* Green shadow */
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}
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.gradio-button {
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background-color: #00FF00; /* Green button */
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color: white;
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border: none;
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padding: 10px 20px;
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font-size: 16px;
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cursor: pointer;
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}
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.gradio-button:hover {
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background-color: #00CC00; /* Darker green on hover */
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}
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.gradio-textbox {
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background-color: #111111; /* Dark gray background */
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color: #00FF00; /* Green text */
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border: 1px solid #00FF00; /* Green border */
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padding: 10px;
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margin-bottom: 10px;
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}
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.gradio-text-area {
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background-color: #111111; /* Dark gray background */
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color: #00FF00; /* Green text */
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border: 1px solid #00FF00; /* Green border */
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padding: 10px;
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}
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.gradio-slider {
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background-color: #111111; /* Dark gray background */
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color: #00FF00; /* Green text */
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}
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.gradio-slider .slider-bar {
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background-color: #00FF00; /* Green slider bar */
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}
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.gradio-slider .slider-thumb {
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background-color: #00FF00; /* Green slider thumb */
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}
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.gradio-label {
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color: #00FF00; /* Green labels */
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}
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h1, h2 {
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color: #00FF00; /* Green headings */
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text-align: center;
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}
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.analysis-container {
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margin-top: 20px;
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padding: 10px;
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border: 1px solid #00FF00;
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}
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"""
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with gr.Blocks() as iface:
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gr.Markdown(
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"""
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#
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Engage in a conversation with our advanced AI model. Customize the response using various parameters.
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"""
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)
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border: 1px solid #00FF00;
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}
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</style>
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""", elem_id="textbox_styles"
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)
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with gr.Row():
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temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, step=0.1, value=0.7)
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top_p = gr.Slider(label="Top p", minimum=0.1, maximum=1.0, step=0.1, value=0.9)
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with gr.Row():
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top_k = gr.Slider(label="Top k", minimum=1, maximum=100, step=1, value=50)
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max_length = gr.Slider(label="Max length", minimum=10, maximum=1000, step=10, value=250)
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with gr.Row():
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submit_button = gr.Button(value="Submit")
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with gr.Row():
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response = gr.TextArea(label="AI Response:", lines=10)
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analysis_html = gr.HTML(elem_id="analysis")
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submit_button.click(fn=get_response, inputs=[input_text, temperature, top_p, top_k, max_length], outputs=[response])
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response.change(fn=update_analysis, inputs=[response], outputs=[analysis_html])
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@@ -162,9 +71,9 @@ with gr.Blocks() as iface:
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def update_analysis(response):
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analysis = analyze_text(response)
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analysis_str = f"
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f"Number of words: {analysis['Number of words']}<br>" \
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f"Number of tokens: {analysis['Number of tokens']}
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iface.update(analysis=analysis_str, live=True)
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iface.outputs[0].postprocess = update_analysis
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import random
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# Load the model and tokenizer
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model_id = "microsoft/phi-2" # Change to your desired model
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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# --- Interface ---
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with gr.Blocks() as iface:
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gr.Markdown(
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"""
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# AI Chat
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"""
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)
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input_text = gr.Textbox(
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label="Your message:", lines=5, placeholder="Ask me anything...", show_label=True
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)
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temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, step=0.1, value=0.7)
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top_p = gr.Slider(label="Top p", minimum=0.1, maximum=1.0, step=0.1, value=0.9)
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top_k = gr.Slider(label="Top k", minimum=1, maximum=100, step=1, value=50)
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max_length = gr.Slider(label="Max length", minimum=10, maximum=1000, step=10, value=250)
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submit_button = gr.Button(value="Submit")
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response = gr.TextArea(label="AI Response:", lines=10)
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analysis_html = gr.HTML(elem_id="analysis")
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submit_button.click(fn=get_response, inputs=[input_text, temperature, top_p, top_k, max_length], outputs=[response])
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response.change(fn=update_analysis, inputs=[response], outputs=[analysis_html])
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def update_analysis(response):
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analysis = analyze_text(response)
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analysis_str = f"Number of characters: {analysis['Number of characters']}<br>" \
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f"Number of words: {analysis['Number of words']}<br>" \
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f"Number of tokens: {analysis['Number of tokens']}"
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iface.update(analysis=analysis_str, live=True)
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iface.outputs[0].postprocess = update_analysis
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