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- requirements.txt +3 -0
README.md
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# Gemini Distractor Generator
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This is a simple web app that uses Gemini 1.5 Flash (via Gemini API) to generate multiple-choice distractors for a given question. It automatically identifies the correct answer and returns three plausible distractors.
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## Features
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- Uses Gemini 1.5 Flash (Free Tier) via API
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- Auto-generates distractors based on question context
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- Also attempts to identify the correct answer
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- Includes minimal Gradio UI
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## Files
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- `app.py`: Main Gradio application.
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- `requirements.txt`: Required Python libraries.
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- `README.md`: This file.
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## How to Run
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### Locally
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1. Install the dependencies:
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```bash
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pip install -r requirements.txt
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```
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2. Run the app:
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```bash
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python app.py
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```
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3. Visit the local URL shown by Gradio.
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### On Hugging Face Spaces
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1. Upload all three files to your Hugging Face Space.
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2. Choose `Gradio` as the SDK.
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3. Make sure to set your Gemini API key in a secure way (e.g., using secrets or hardcoded for testing).
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4. Set `app.py` as the entry point.
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## Notes
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- Gemini 1.5 Flash is selected to stay within free-tier quotas.
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- You may need to set your API key inside `app.py` manually.
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app.py
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import os
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import gradio as gr
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import google.generativeai as genai
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genai.configure(api_key=os.getenv("AIzaSyDDPWejHT0yEyKW3QfF63dYaT3IPdKNuW0"))
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def generate_distractors(question):
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prompt = f"""
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You are an expert educator designing challenging multiple-choice questions.
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Given a question, generate:
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- One correct answer
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- Three plausible distractors
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- A brief explanation for why each distractor seems believable but is incorrect
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Format:
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Correct Answer: ...
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Distractor 1: ...
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Explanation 1: ...
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Distractor 2: ...
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Explanation 2: ...
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Distractor 3: ...
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Explanation 3: ...
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Question: {question}
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"""
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model = genai.GenerativeModel("gemini-2.0-flash")
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response = model.generate_content(prompt)
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return response.text.strip()
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with gr.Blocks() as demo:
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gr.Markdown("## Confusing Distractor Generator")
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gr.Markdown("Enter a multiple-choice question. The model will generate one correct answer, three plausible distractors, and explanations.")
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with gr.Row():
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question = gr.Textbox(label="MCQ Question", placeholder="Enter your question here", lines=2)
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with gr.Row():
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generate_btn = gr.Button("Generate")
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with gr.Row():
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output = gr.Textbox(label="Model Output", lines=15)
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generate_btn.click(fn=generate_distractors, inputs=question, outputs=output)
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if __name__ == "__main__":
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demo.launch()
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requirements.txt
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google-generativeai>=0.3.2
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gradio>=4.28.3
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python-dotenv
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