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| import os | |
| os.system("pip install torch gradio transformers") | |
| import gradio as gr | |
| import torch | |
| import random | |
| from transformers import GPT2Tokenizer, GPT2LMHeadModel | |
| tokenizer = GPT2Tokenizer.from_pretrained("gpt2-medium") | |
| model = GPT2LMHeadModel.from_pretrained("gpt2-medium") | |
| def generate_text(prompt): | |
| input_ids = tokenizer.encode(prompt, return_tensors="pt") | |
| output = model.generate(input_ids, max_length=48, temperature=0.85, do_sample=True) | |
| text = tokenizer.decode(output[0], skip_special_tokens=True) | |
| return text | |
| # Read real titles from file | |
| with open('dhar_mann_titles.txt', 'r') as file: | |
| dhar_mann_titles = file.readlines() | |
| # Function to generate an AI title (dummy implementation) | |
| def generate_ai_title(): | |
| inputs = tokenizer(["<|startoftext|>"]*1, return_tensors="pt") | |
| outputs = model.generate(**inputs, max_new_tokens=50, use_cache=True, temperature=0.85, do_sample=True) | |
| return (tokenizer.batch_decode(outputs)[0])[15:-13] | |
| # Function to check user's answer and update score | |
| def check_answer(user_choice, real_index, option1, option2, score): | |
| if (user_choice == "Option 1" and real_index == 0) or (user_choice == "Option 2" and real_index == 1): | |
| score += 1 | |
| return f"Correct! Your current score is: {score}", score, gr.update(visible=True), gr.update(visible=False) | |
| else: | |
| score = 0 | |
| return f"Incorrect. Your score has been reset to: {score}", score, gr.update(visible=False), gr.update(visible=True) | |
| # Function to update options | |
| def update_options(): | |
| real_index = random.choice([0, 1]) | |
| real_title = random.choice(dhar_mann_titles).strip() | |
| ai_title = generate_ai_title() | |
| if real_index == 0: | |
| return real_title, ai_title, real_index | |
| else: | |
| return ai_title, real_title, real_index | |
| def create_interface(): | |
| with gr.Blocks() as demo: | |
| score = gr.State(0) | |
| real_index_state = gr.State(0) # Initialize real_index_state properly | |
| score_display = gr.Markdown("## Real or AI - Dhar Mann\n**Current Score: 0**") | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("### Option 1") | |
| option1_box = gr.Markdown("") | |
| with gr.Column(): | |
| gr.Markdown("### Option 2") | |
| option2_box = gr.Markdown("") | |
| with gr.Row(): | |
| choice = gr.Radio(["Option 1", "Option 2"], label="Which one do you think is real?") | |
| submit_button = gr.Button("Submit") | |
| result_text = gr.Markdown("") | |
| continue_button = gr.Button("Continue", visible=False) | |
| restart_button = gr.Button("Restart", visible=False) | |
| def on_submit(user_choice, option1, option2, real_index, score): | |
| result, new_score, continue_visibility, restart_visibility = check_answer(user_choice, real_index, option1, option2, score) | |
| return result, new_score, continue_visibility, restart_visibility | |
| def on_continue(score): | |
| option1, option2, real_index = update_options() | |
| new_score_display = f"## Real or AI - Dhar Mann\n**Current Score: {score}**" | |
| return option1, option2, real_index, new_score_display, gr.update(value=None), "", gr.update(visible=False), gr.update(visible=False) | |
| def on_restart(): | |
| return on_continue(0) | |
| # Initialize options | |
| option1, option2, real_index = update_options() | |
| submit_button.click(on_submit, inputs=[choice, option1_box, option2_box, real_index_state, score], outputs=[result_text, score, continue_button, restart_button]) | |
| continue_button.click(on_continue, inputs=score, outputs=[option1_box, option2_box, real_index_state, score_display, choice, result_text, continue_button, restart_button]) | |
| restart_button.click(on_restart, outputs=[option1_box, option2_box, real_index_state, score_display, choice, result_text, continue_button, restart_button]) | |
| # Set initial content for option boxes and real_index_state | |
| option1_box.value = option1 | |
| option2_box.value = option2 | |
| real_index_state.set(real_index) # Set the initial real_index_state | |
| return demo | |
| demo = create_interface() | |
| demo.launch() |