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Update app.py
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app.py
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from transformers import AutoTokenizer,
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import gradio as gr
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model_name = "google/flan-t5-base"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model =
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pipe = pipeline("
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# Personas
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personas = {
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"π’ Optimist": "
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"π΄ Pessimist": "
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"π‘ Neutral": "
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}
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# Debate function
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def generate_debate(topic):
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results = []
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for label, instruction in personas.items():
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prompt =
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return "\n\n".join(results)
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# Gradio Interface
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demo = gr.Interface(
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fn=generate_debate,
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inputs=gr.Textbox(label="Debate Topic"),
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outputs=gr.Markdown(),
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title="ποΈ
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description="Debate
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)
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demo.launch()
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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import gradio as gr
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model_name = "EleutherAI/gpt-neo-1.3B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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personas = {
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"π’ Optimist": "Give an optimistic argument with 2 benefits and one example.",
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"π΄ Pessimist": "Give a critical argument with 2 drawbacks and one example.",
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"π‘ Neutral": "List pros and cons and give a neutral summary."
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}
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def generate_debate(topic):
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results = []
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for label, instruction in personas.items():
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prompt = (
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f"Debate Topic: {topic}\n"
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f"Persona: {label}\n"
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f"{instruction}\n"
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f"Write 3β4 sentences."
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)
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output = pipe(prompt, max_new_tokens=180, temperature=0.7)[0]['generated_text']
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results.append(f"### {label}\n{output.strip()}")
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return "\n\n".join(results)
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demo = gr.Interface(
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fn=generate_debate,
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inputs=gr.Textbox(label="Debate Topic"),
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outputs=gr.Markdown(),
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title="ποΈ Debate Simulator with GPT-Neo",
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description="Debate with Optimist, Pessimist & Neutral perspectives using GPT-Neo-1.3B."
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)
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demo.launch()
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