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Update app.py
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app.py
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from huggingface_hub import InferenceClient
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import gradio as gr
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import random
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import os
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# Set up the client for
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client = InferenceClient("meta-llama/Meta-Llama-3.1-8B", token=os.environ.get("HUGGING_FACE_HUB_TOKEN"))
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def generate_text(prompt, max_length=500):
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response = client.text_generation(prompt, max_new_tokens=max_length, temperature=0.7)
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def debate_assistant(topic, stance):
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# Generate the main argument
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argument_prompt = f"""Generate a comprehensive
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Topic: {topic}
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Stance: {stance}
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1.
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2. Three
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3.
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4.
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5.
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argument = generate_text(argument_prompt, max_length=800)
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# Generate a counterargument
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counter_prompt = f"""Generate a
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Topic: {topic}
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Original Stance: {stance}
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1.
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2. Three rebuttals
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3.
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4.
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Ensure your counterargument is logical, well-supported, and addresses key points of the original stance."""
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counterargument = generate_text(counter_prompt, max_length=
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# Generate analysis
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analysis_prompt = f"""
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Topic: {topic}
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1. Topic categorization
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2.
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3.
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4. A brief overview of the historical context or background of this debate
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5. Two potential real-world implications or consequences of this debate
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Please provide a balanced and insightful analysis."""
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analysis = generate_text(analysis_prompt, max_length=
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return f"""Argument ({stance}):
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{argument}
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def suggest_topic():
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topics = [
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"Should artificial
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"Is universal basic income a viable
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"Should
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"Is space
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"Should
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"Is nuclear
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"Should
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"Is a
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"Should
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"Is
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]
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return random.choice(topics)
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gr.Radio(["For", "Against"], label="Stance")
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],
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outputs=gr.Textbox(label="Generated Debate Content"),
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title="
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description="Enter a debate topic and choose a stance to generate
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examples=[
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["Should artificial
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["Is universal basic income a viable
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["Should
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]
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)
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from huggingface_hub import InferenceClient
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import gradio as gr
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import random
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# Set up the client for FLAN-T5-XL model inference
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client = InferenceClient("google/flan-t5-xl")
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def generate_text(prompt, max_length=500):
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response = client.text_generation(prompt, max_new_tokens=max_length, temperature=0.7)
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def debate_assistant(topic, stance):
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# Generate the main argument
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argument_prompt = f"""Generate a comprehensive argument for the following debate topic:
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Topic: {topic}
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Stance: {stance}
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Include:
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1. Main claim
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2. Three supporting points
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3. Potential counterargument
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4. Rebuttal
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5. Conclusion"""
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argument = generate_text(argument_prompt, max_length=600)
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# Generate a counterargument
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counter_prompt = f"""Generate a counterargument for the following debate topic:
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Topic: {topic}
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Original Stance: {stance}
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Include:
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1. Counter-claim
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2. Three rebuttals
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3. New supporting point
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4. Conclusion"""
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counterargument = generate_text(counter_prompt, max_length=600)
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# Generate analysis
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analysis_prompt = f"""Analyze the debate on the following topic:
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Topic: {topic}
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Provide:
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1. Topic categorization
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2. Two ethical considerations
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3. Three discussion questions"""
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analysis = generate_text(analysis_prompt, max_length=400)
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return f"""Argument ({stance}):
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{argument}
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def suggest_topic():
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topics = [
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"Should artificial intelligence be regulated?",
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"Is universal basic income a viable economic policy?",
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"Should voting be mandatory?",
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"Is space exploration a worthwhile investment?",
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"Should gene editing in humans be allowed?",
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"Is nuclear energy the solution to climate change?",
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"Should social media platforms be held responsible for user content?",
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"Is a four-day work week beneficial for society?",
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"Should animal testing be banned?",
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"Is globalization overall positive or negative for developing countries?"
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]
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return random.choice(topics)
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gr.Radio(["For", "Against"], label="Stance")
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],
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outputs=gr.Textbox(label="Generated Debate Content"),
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title="AI-powered Debate Assistant (FLAN-T5-XL)",
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description="Enter a debate topic and choose a stance to generate arguments, counterarguments, and analysis.",
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examples=[
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["Should artificial intelligence be regulated?", "For"],
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["Is universal basic income a viable economic policy?", "Against"],
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["Should gene editing in humans be allowed?", "For"]
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]
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)
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