Spaces:
Sleeping
Sleeping
| # app.py | |
| import gradio as gr | |
| import fitz # PyMuPDF | |
| import os | |
| import requests | |
| # --------------- GROQ GPT CALL --------------- | |
| GROQ_API_KEY = os.getenv("GROQ_API_KEY") or "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" | |
| GROQ_MODEL = "llama3-70b-8192" | |
| def query_gpt(prompt): | |
| headers = { | |
| "Authorization": f"Bearer {GROQ_API_KEY}", | |
| "Content-Type": "application/json" | |
| } | |
| data = { | |
| "model": GROQ_MODEL, | |
| "messages": [ | |
| {"role": "user", "content": prompt} | |
| ] | |
| } | |
| response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=data, headers=headers) | |
| return response.json()['choices'][0]['message']['content'] | |
| # --------------- PDF TEXT EXTRACTION --------------- | |
| def extract_text_from_pdf(pdf_path): | |
| doc = fitz.open(pdf_path) | |
| text = "" | |
| for page in doc: | |
| text += page.get_text() | |
| return text | |
| # --------------- MAIN TASKS --------------- | |
| def summarize_textbook(text): | |
| prompt = f"Summarize the following content into important bullet points:\n\n{text}" | |
| return query_gpt(prompt) | |
| def generate_mcqs(text): | |
| prompt = f"Generate 5 multiple choice questions (MCQs) with 4 options each from the following content:\n\n{text}" | |
| return query_gpt(prompt) | |
| def simplify_concepts(text): | |
| prompt = f"Simplify and explain the following concepts for a student who is 14 years old:\n\n{text}" | |
| return query_gpt(prompt) | |
| def process_text_inputs(book, chapter, action_type): | |
| user_prompt = f"Give a detailed explanation and key points for the chapter '{chapter}' from the book '{book}'" | |
| if action_type == "Summarize Important Points": | |
| return query_gpt(f"Summarize the following chapter:\n\n{user_prompt}") | |
| elif action_type == "Generate MCQs": | |
| return query_gpt(f"Generate 5 MCQs with 4 options each from:\n\n{user_prompt}") | |
| elif action_type == "Simplify Concepts": | |
| return query_gpt(f"Explain in simple terms the concepts from:\n\n{user_prompt}") | |
| # --------------- GRADIO UI --------------- | |
| with gr.Blocks(title="AI Textbook Tutor") as app: | |
| gr.Markdown("# 📘 AI Textbook Tutor\nUpload your textbook or type a chapter and get summaries, MCQs, and simplified explanations!") | |
| with gr.Tab("📄 Upload PDF"): | |
| with gr.Row(): | |
| pdf_input = gr.File(label="Upload PDF", file_types=None) | |
| action_pdf = gr.Radio([ | |
| "Summarize Important Points", | |
| "Generate MCQs", | |
| "Simplify Concepts" | |
| ], label="Select Task") | |
| run_pdf = gr.Button("Run 🧠 on PDF") | |
| output_pdf = gr.Textbox(label="📤 Output", lines=15) | |
| with gr.Tab("🔍 Search by Book & Chapter"): | |
| with gr.Row(): | |
| book_input = gr.Textbox(label="Book Name", placeholder="e.g., Physics 9th Class") | |
| chapter_input = gr.Textbox(label="Chapter or Topic Name", placeholder="e.g., Measurement") | |
| action_text = gr.Radio([ | |
| "Summarize Important Points", | |
| "Generate MCQs", | |
| "Simplify Concepts" | |
| ], label="Select Task") | |
| run_text = gr.Button("Run 🧠 on Chapter") | |
| output_text = gr.Textbox(label="📤 Output", lines=15) | |
| def process_pdf(pdf_file, action_type): | |
| text = extract_text_from_pdf(pdf_file) | |
| if len(text) > 5000: | |
| text = text[:5000] | |
| if action_type == "Summarize Important Points": | |
| return summarize_textbook(text) | |
| elif action_type == "Generate MCQs": | |
| return generate_mcqs(text) | |
| elif action_type == "Simplify Concepts": | |
| return simplify_concepts(text) | |
| run_pdf.click(fn=process_pdf, inputs=[pdf_input, action_pdf], outputs=[output_pdf]) | |
| run_text.click(fn=process_text_inputs, inputs=[book_input, chapter_input, action_text], outputs=[output_text]) | |
| app.launch() | |