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| import gradio as gr | |
| import os | |
| import json | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| from ppt_parser import transfer_to_structure | |
| from functools import lru_cache | |
| # ✅ Get Hugging Face token from Space Secrets | |
| hf_token = os.getenv("HF_TOKEN") | |
| # ✅ Load summarization model (BART) | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| # ✅ Load Mistral model (memoized to avoid reloading) | |
| def load_mistral(): | |
| tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1", token=hf_token) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "mistralai/Mistral-7B-Instruct-v0.1", | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| token=hf_token | |
| ) | |
| return pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512) | |
| mistral_pipe = load_mistral() | |
| # ✅ Global variable to hold extracted content | |
| extracted_text = "" | |
| def extract_text_from_pptx_json(parsed_json: dict) -> str: | |
| text = "" | |
| for slide in parsed_json.values(): | |
| for shape in slide.values(): | |
| if shape.get('type') == 'group': | |
| for group_shape in shape.get('group_content', {}).values(): | |
| if group_shape.get('type') == 'text': | |
| for para_key, para in group_shape.items(): | |
| if para_key.startswith("paragraph_"): | |
| text += para.get("text", "") + "\n" | |
| elif shape.get('type') == 'text': | |
| for para_key, para in shape.items(): | |
| if para_key.startswith("paragraph_"): | |
| text += para.get("text", "") + "\n" | |
| return text.strip() | |
| def handle_pptx_upload(pptx_file): | |
| global extracted_text | |
| tmp_path = pptx_file.name | |
| parsed_json_str, _ = transfer_to_structure(tmp_path, "images") | |
| parsed_json = json.loads(parsed_json_str) | |
| extracted_text = extract_text_from_pptx_json(parsed_json) | |
| return extracted_text or "No readable text found in slides." | |
| def summarize_text(): | |
| global extracted_text | |
| if not extracted_text: | |
| return "Please upload and extract text from a PPTX file first." | |
| summary = summarizer(extracted_text, max_length=200, min_length=50, do_sample=False)[0]['summary_text'] | |
| return summary | |
| def clarify_concept(question): | |
| global extracted_text | |
| if not extracted_text: | |
| return "Please upload and extract text from a PPTX file first." | |
| prompt = f"[INST] Use the following context to answer the question:\n\n{extracted_text}\n\nQuestion: {question} [/INST]" | |
| response = mistral_pipe(prompt)[0]["generated_text"] | |
| return response.replace(prompt, "").strip() | |
| # ✅ Gradio UI | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🧠 AI-Powered Study Assistant for PowerPoint Lectures (Mistral 7B)") | |
| pptx_input = gr.File(label="📂 Upload PPTX File", file_types=[".pptx"]) | |
| extract_btn = gr.Button("📜 Extract & Summarize") | |
| extracted_output = gr.Textbox(label="📄 Extracted Text", lines=10, interactive=False) | |
| summary_output = gr.Textbox(label="📝 Summary", interactive=False) | |
| extract_btn.click(handle_pptx_upload, inputs=[pptx_input], outputs=[extracted_output]) | |
| extract_btn.click(summarize_text, outputs=[summary_output]) | |
| question = gr.Textbox(label="❓ Ask a Question") | |
| ask_btn = gr.Button("💬 Ask Mistral") | |
| ai_answer = gr.Textbox(label="🤖 Mistral Answer", lines=4) | |
| ask_btn.click(clarify_concept, inputs=[question], outputs=[ai_answer]) | |
| if __name__ == "__main__": | |
| demo.launch() |