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| import gradio as gr | |
| import requests | |
| import json | |
| from reportlab.lib.pagesizes import letter | |
| from reportlab.pdfgen import canvas | |
| from textwrap import wrap | |
| # Hugging Face Inference API details | |
| HF_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3" | |
| import os | |
| HF_API_KEY = os.getenv("HF_API_KEY") # Read from environment variable | |
| HEADERS = {"Authorization": f"Bearer {HF_API_KEY}"} | |
| # Function to call Hugging Face API | |
| def query_hf_api(prompt): | |
| payload = {"inputs": prompt} | |
| response = requests.post(HF_API_URL, headers=HEADERS, json=payload) | |
| if response.status_code == 200: | |
| return response.json()[0]["generated_text"] | |
| else: | |
| return f"Error: {response.status_code} - {response.text}" | |
| # Debate function | |
| def conduct_debate(topic): | |
| # AI Assistant 1's response | |
| prompt_1 = f"The topic of debate is: {topic}. Present your argument in favor." | |
| response_1 = query_hf_api(prompt_1) | |
| # AI Assistant 2's response | |
| prompt_2 = f"The topic of debate is: {topic}. Present your argument against." | |
| response_2 = query_hf_api(prompt_2) | |
| # Judge's conclusion | |
| prompt_judge = f"The debate topic was: {topic}. One argument in favor: {response_1}. One argument against: {response_2}. As a judge, analyze the arguments and provide a fair conclusion." | |
| conclusion = query_hf_api(prompt_judge) | |
| return response_1, response_2, conclusion | |
| # Function to generate a properly formatted PDF | |
| def generate_pdf(topic, response_1, response_2, conclusion): | |
| pdf_filename = "debate_result.pdf" | |
| c = canvas.Canvas(pdf_filename, pagesize=letter) | |
| c.setFont("Helvetica", 12) | |
| y_position = 750 | |
| def draw_wrapped_text(text, x, y, max_width=450): | |
| lines = wrap(text, width=80) # Wrap text for better formatting | |
| for line in lines: | |
| c.drawString(x, y, line) | |
| y -= 20 | |
| return y | |
| c.drawString(100, y_position, f"Debate Topic: {topic}") | |
| y_position -= 30 | |
| c.drawString(100, y_position, "Argument in Favor:") | |
| y_position -= 20 | |
| y_position = draw_wrapped_text(response_1, 100, y_position) | |
| y_position -= 30 # Space between sections | |
| c.drawString(100, y_position, "Argument Against:") | |
| y_position -= 20 | |
| y_position = draw_wrapped_text(response_2, 100, y_position) | |
| y_position -= 30 # Space between sections | |
| c.drawString(100, y_position, "Judge's Conclusion:") | |
| y_position -= 20 | |
| y_position = draw_wrapped_text(conclusion, 100, y_position) | |
| c.save() | |
| return pdf_filename | |
| # Gradio UI | |
| def debate_interface(topic): | |
| response_1, response_2, conclusion = conduct_debate(topic) | |
| pdf_file = generate_pdf(topic, response_1, response_2, conclusion) | |
| return response_1, response_2, conclusion, pdf_file | |
| iface = gr.Interface( | |
| fn=debate_interface, | |
| inputs=gr.Textbox(label="Enter Debate Topic"), | |
| outputs=[ | |
| gr.Textbox(label="Argument in Favor"), | |
| gr.Textbox(label="Argument Against"), | |
| gr.Textbox(label="Judge's Conclusion"), | |
| gr.File(label="Download Debate PDF") | |
| ], | |
| title="AI Debate Platform", | |
| description="Enter a debate topic and let two AI assistants argue, with a third AI model acting as a judge." | |
| ) | |
| iface.launch(share=True) | |