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import gradio as gr |
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from chat import generate_response_stream, reset_conversation, switch_model |
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from config import MODEL_LIST, MAX_TOKENS, TEMPERATURE |
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def chat_interface(user_input, max_tokens, temperature, model_name): |
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switch_model(model_name) |
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return generate_response_stream(user_input, max_length=max_tokens, temperature=temperature) |
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iface = gr.Interface( |
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fn=chat_interface, |
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inputs=[ |
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gr.Textbox(lines=3, placeholder="Type your message here...", label="User Input"), |
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gr.Slider(minimum=50, maximum=2000, step=50, label="Max Tokens", value=MAX_TOKENS), |
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gr.Slider(minimum=0.1, maximum=1.0, step=0.1, label="Temperature", value=TEMPERATURE), |
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gr.Dropdown(list(MODEL_LIST.keys()), label="Choose Model", value="ChatGpt Tune Base") |
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], |
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outputs=gr.Textbox(label="AI Response"), |
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title="ChatGpt Tune Multi-Model Streaming Optimized", |
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description="Chat with multiple Hugging Face/safetensor models with streaming responses and low memory usage!" |
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) |
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reset_iface = gr.Interface( |
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fn=reset_conversation, |
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inputs=[], |
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outputs="text", |
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title="Reset Conversation", |
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description="Clear memory for the current model" |
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) |
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demo = gr.TabbedInterface([iface, reset_iface], ["Chat", "Reset Conversation"]) |
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if __name__ == "__main__": |
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demo.launch(share=True) |
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