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Parent(s):
39b224a
Update app.py
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app.py
CHANGED
@@ -1,63 +1,27 @@
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import gradio as gr
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from
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": message})
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response = ""
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for message in client.chat_completion(
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messages,
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max_tokens=max_tokens,
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stream=True,
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temperature=temperature,
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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response += token
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yield response
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"""
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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"""
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.95,
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step=0.05,
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label="Top-p (nucleus sampling)",
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),
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],
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demo.launch()
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Specify the hosted model repository or URL
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model_repo = "Tanvi03/ReidLM" # Replace with the actual model repository or URL
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# Load the tokenizer and model from the hosted repository
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tokenizer = AutoTokenizer.from_pretrained(model_repo)
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model = AutoModelForCausalLM.from_pretrained(model_repo)
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# Define the function to handle the chat interaction
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def chat(message):
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input_ids = tokenizer.encode(message, return_tensors="pt")
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output = model.generate(input_ids, max_length=100, num_return_sequences=1)
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response = tokenizer.decode(output[0], skip_special_tokens=True)
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return response
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# Create a Gradio interface
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iface = gr.Interface(
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fn=chat,
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inputs=gr.Textbox(placeholder="Enter your message..."),
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outputs=gr.Textbox(placeholder="Model's response will appear here..."),
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title="Chat with Hosted Model"
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)
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# Launch the Gradio app
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iface.launch()
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