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| import requests | |
| import gradio as gr | |
| from dotenv import load_dotenv | |
| import os | |
| from openai import OpenAI | |
| import spacy | |
| import random | |
| # Load environment variables from .env file | |
| load_dotenv() | |
| # Access the env | |
| HF_TOKEN = os.getenv('HUGGING_FACE_TOKEN') | |
| # openai setup | |
| client = OpenAI( | |
| api_key=os.getenv('OPENAI_API_KEY') | |
| ) | |
| # Global variable to control debug printing | |
| DEBUG_MODE = True | |
| def debug_print(*args, **kwargs): | |
| if DEBUG_MODE: | |
| print(*args, **kwargs) | |
| def generate_questions(input_prompt): | |
| """ | |
| Generates EFL questions based on the input text using OpenAI's GPT-3. | |
| """ | |
| prompt=f"Generate 5 simple questions in English for an EFL (English as a Foreign Language) based on the following topic: {input_prompt}" | |
| response = client.chat.completions.create( | |
| messages=[ | |
| {"role": "system", "content": ""}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| model="gpt-3.5-turbo", | |
| ) | |
| questions = response.choices[0].message.content | |
| debug_print("GPT output:", questions) | |
| return questions | |
| with gr.Blocks() as app: | |
| with gr.Column(): | |
| gr.Markdown("### EFL Question Generator") | |
| input_text = gr.Textbox(label="Topic", lines=1, placeholder="Type or paste your topic here...", value="China") | |
| output_questions = gr.Textbox(label="Generated Questions", lines=5) | |
| generate_btn = gr.Button("Generate Questions") | |
| generate_btn.click(fn=generate_questions, inputs=input_text, outputs=output_questions) | |
| app.launch() | |