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import gradio as gr
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from database import create_db
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from functions import *
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from functions import _generate_code
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models_options_general = ['GPT2', 'GPT2-medium', 'GPT2-large', 'GPT2-persian', 'GPT-Neo-125M']
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models_options_codegen = ['codegen']
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models_options_chatbot = ['dialoGPT', 'dialoGPT-medium', 'dialoGPT-large']
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create_db()
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with gr.Blocks() as interface:
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gr.Markdown(
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"# **GPT Tools**\n\n"
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"Generate something using GPT models. Select the model and adjust the parameters for optimal results."
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)
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with gr.Tabs():
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with gr.Tab("Text Generator"):
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with gr.Row():
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with gr.Column(scale=1, min_width=350):
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input_text = gr.Textbox(label="Input Text", placeholder="Enter your text here...", lines=4, max_lines=6)
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selected_model = gr.Radio(choices=models_options_general, value="GPT2", label="Select Model", type="value")
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with gr.Row():
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max_tokens = gr.Slider(10, 100, value=50, step=1, label="Max New Tokens", interactive=True)
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with gr.Column(scale=1, min_width=350):
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output_text = gr.Textbox(label="Generated Text", interactive=False, lines=8, max_lines=12)
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generate_button = gr.Button("Generate Text", variant="primary")
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generate_button.click(
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generate,
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inputs=[input_text, selected_model, max_tokens],
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outputs=output_text,
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)
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with gr.Tab("Multiverse Story Generator"):
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with gr.Row():
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with gr.Column(scale=1, min_width=350):
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input_text = gr.Textbox(label="Enter your story idea", placeholder="e.g. A scientist discovers a parallel universe...", lines=4, max_lines=6)
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selected_model = gr.Radio(choices=models_options_general, value="GPT2", label="Select Model for Story Generation", type="value")
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max_length = gr.Slider(50, 300, value=150, step=1, label="Max Length", interactive=True)
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with gr.Column(scale=1, min_width=350):
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output_text = gr.Textbox(label="Generated Worlds", interactive=False, lines=12, max_lines=20)
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generate_button = gr.Button("Generate Parallel Worlds", variant="primary")
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generate_button.click(
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generate_multiverse,
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inputs=[input_text, selected_model, max_length],
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outputs=output_text,
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)
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with gr.Tab("Interactive Story Writing"):
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with gr.Row():
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with gr.Column(scale=1, min_width=350):
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story_input = gr.Textbox(label="Add to Story", placeholder="Enter your part of the story...", lines=4, max_lines=6)
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story_model = gr.Radio(choices=models_options_general, value="GPT2", label="Select Model", type="value")
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story_max_length = gr.Slider(50, 300, value=50, step=1, label="Max Length", interactive=True)
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with gr.Column(scale=1, min_width=350):
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story_text = gr.Textbox(label="Story So Far", interactive=False, lines=12, max_lines=20)
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story_button = gr.Button("Generate Next Part", variant="primary")
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reset_button = gr.Button("Reset Story", variant="secondary")
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story_button.click(
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interactive_story,
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inputs=[story_input, story_model, story_max_length],
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outputs=story_text,
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)
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reset_button.click(
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reset_story,
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inputs=[],
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outputs=story_text,
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)
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with gr.Tab("Training"):
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gr.Markdown("# **Train Model**\n\n")
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with gr.Column(scale=1, min_width=250):
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train_model_selector = gr.Radio(choices=models_options_general, value="GPT2", label="Select Model for Training", type="value")
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train_method = gr.Radio(
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choices=["Custom Text", "Database", "Dataset File", "Hugging Face Dataset"],
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value="Custom Text",
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label="Training Method",
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type="value"
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)
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dataset_name = gr.Textbox(label="Hugging Face Dataset Name", placeholder="Enter dataset name (e.g., ag_news)")
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split_name = gr.Textbox(label="Dataset Split", placeholder="e.g., train, test, validation")
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epochs = gr.Slider(1, 100, value=10, step=1, label="Epochs", interactive=True)
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batch_size = gr.Slider(1, 100, value=8, step=1, label="Batch Size", interactive=True)
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password = gr.Textbox(label="Enter Training Password", placeholder="Enter password", type="password")
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custom_text = gr.Textbox(label="Custom Text (optional)", placeholder="Enter custom text for training...")
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dataset_file = gr.File(label="Upload Dataset", type="filepath", file_types=[".parquet", ".csv", ".json", ".txt"])
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train_button = gr.Button("Train Model", variant="primary")
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train_status = gr.Textbox(label="Training Status", interactive=False)
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train_button.click(
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verify_and_train_combined,
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inputs=[train_model_selector, train_method, epochs, batch_size, password, custom_text, dataset_file, dataset_name, split_name],
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outputs=train_status,
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)
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train_button.click(
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verify_and_train_combined,
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inputs=[train_model_selector, train_method, epochs, batch_size, password, custom_text, dataset_file, dataset_name, split_name],
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outputs=train_status,
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)
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with gr.Tab("Code Generator"):
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gr.Markdown("### Generate Code from Descriptions")
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with gr.Row():
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with gr.Column(scale=1, min_width=350):
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code_prompt = gr.Textbox(label="Code Prompt", placeholder="Describe your coding task, e.g., 'Write a Python function to calculate Fibonacci numbers.'")
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code_max_tokens = gr.Slider(10, 500, value=150, step=10, label="Max Tokens")
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with gr.Column(scale=1, min_width=350):
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generated_code = gr.Textbox(label="Generated Code", interactive=False, lines=10, max_lines=20)
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generate_code_button = gr.Button("Generate Code")
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generate_code_button.click(
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_generate_code,
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inputs=[code_prompt, code_max_tokens],
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outputs=generated_code,
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)
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with gr.Tab("Story World Builder"):
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with gr.Row():
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with gr.Column(scale=1, min_width=350):
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world_name = gr.Textbox(label="World Name", placeholder="Enter your world name...")
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locations = gr.Textbox(label="Locations", placeholder="Enter locations separated by commas...")
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characters = gr.Textbox(label="Characters", placeholder="Enter characters separated by commas...")
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create_button = gr.Button("Create World", variant='primary')
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generate_story_button = gr.Button("Generate Story")
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with gr.Column(scale=1, min_width=350):
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world_status = gr.Textbox(label="World Status", interactive=False)
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generated_story = gr.Textbox(label="Generated Story", interactive=False, lines=12, max_lines=20)
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create_button.click(
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define_world,
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inputs=[world_name, locations, characters],
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outputs=world_status,
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)
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gr.Markdown("### Generate a Story in Your World")
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with gr.Row():
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with gr.Column(scale=1, min_width=350):
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story_world = gr.Textbox(label="Enter World Name", placeholder="World name...")
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event = gr.Textbox(label="Event", placeholder="Describe an event in the world...")
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selected_model = gr.Radio(choices=models_options_general, value="GPT2", label="Select Model", type="value")
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max_length = gr.Slider(50, 300, value=150, step=1, label="Max Length")
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with gr.Tab("Chatbot"):
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gr.Markdown("### **Chat With AI Models**")
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with gr.Row():
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with gr.Column(scale=1, min_width=250):
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username = gr.Textbox(label="Username", placeholder="Enter your username", lines=1)
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chat_id = gr.Textbox(label="Chat ID (optional)", placeholder="Enter chat ID or leave blank for a new chat", lines=1)
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selected_model = gr.Radio(models_options_chatbot, label="Select Model", value="dialoGPT")
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send_button = gr.Button("Send", variant="primary")
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reset_button = gr.Button("Reset Chat", variant="secondary")
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with gr.Column(scale=1, min_width=250):
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input_text = gr.Textbox(label="Your Message", placeholder="Type your message here...", lines=2)
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emotion_output = gr.Textbox(label="Detected Emotion", interactive=False)
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chat_output = gr.Textbox(label="Chat History", lines=10, interactive=False)
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send_button.click(
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chatbot_response_with_emotion,
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inputs=[username, input_text, selected_model, chat_id],
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outputs=[chat_output, chat_id, emotion_output]
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)
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reset_button.click(
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reset_chat,
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inputs=[username],
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outputs=[chat_output]
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)
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gr.Markdown("---")
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gr.Markdown("### **Fetch Chat IDs**")
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with gr.Row():
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with gr.Column(scale=1, min_width=250):
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username = gr.Textbox(label="Username", placeholder="Enter your username", lines=1)
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fetch_btn = gr.Button("Fetch", variant="primary")
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with gr.Column(scale=1, min_width=250):
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fetch_output = gr.Textbox(label="Chat IDs", lines=3, interactive=False)
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fetch_btn.click(
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chat_ids,
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inputs=[username],
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outputs=[fetch_output],
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)
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generate_story_button.click(
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generate_story,
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inputs=[selected_model, story_world, max_length, event],
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outputs=generated_story,
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)
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gr.Markdown("Made by **AliMc2021** with β€οΈ")
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interface.queue().launch(
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server_port=7860,
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show_error=True,
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inline=False,
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) |
