| import os | |
| import torch | |
| import whisper | |
| import PyPDF2 | |
| import gradio as gr | |
| from transformers import BertTokenizerFast, BertForQuestionAnswering, pipeline | |
| from torch.nn.functional import softmax | |
| from docx import Document | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| qa_model = BertForQuestionAnswering.from_pretrained("deepset/bert-base-cased-squad2").to(device) | |
| tokenizer = BertTokenizerFast.from_pretrained("deepset/bert-base-cased-squad2") | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| whisper_model = whisper.load_model("base") | |
| def extract_text(file_obj): | |
| ext = os.path.splitext(file_obj.name)[1].lower() | |
| if ext == ".pdf": | |
| reader = PyPDF2.PdfReader(file_obj) | |
| return "\n".join([p.extract_text() for p in reader.pages if p.extract_text()]) | |
| elif ext == ".docx": | |
| doc = Document(file_obj) | |
| return "\n".join([p.text for p in doc.paragraphs]) | |
| elif ext == ".txt": | |
| return file_obj.read().decode("utf-8") | |
| return "" | |
| def summarize_text(text): | |
| if len(text) < 50: | |
| return "Text too short to summarize." | |
| if len(text) > 1000: | |
| text = text[:1000] | |
| summary = summarizer(text, max_length=120, min_length=30, do_sample=False) | |
| return summary[0]['summary_text'] | |
| def ask_question(question, context): | |
| inputs = tokenizer.encode_plus(question, context, return_tensors="pt", truncation=True, max_length=512).to(device) | |
| with torch.no_grad(): | |
| outputs = qa_model(**inputs) | |
| start_idx = torch.argmax(outputs.start_logits) | |
| end_idx = torch.argmax(outputs.end_logits) + 1 | |
| score = softmax(outputs.start_logits, dim=1)[0][start_idx] * softmax(outputs.end_logits, dim=1)[0][end_idx - 1] | |
| answer = tokenizer.decode(inputs["input_ids"][0][start_idx:end_idx]) | |
| return f"Answer: {answer.strip()}\nConfidence: {round(score.item()*100, 2)}%" | |
| def transcribe(audio_path): | |
| result = whisper_model.transcribe(audio_path) | |
| return result["text"] | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🎙️📄 LexPilot: Voice + Document Q&A Assistant") | |
| gr.Markdown("Upload a document or paste content. Ask questions by typing or using your voice.") | |
| with gr.Tab("Question Answering"): | |
| with gr.Row(): | |
| uploaded_file = gr.File(label="Upload .pdf / .docx / .txt", file_types=[".pdf", ".docx", ".txt"]) | |
| pasted_text = gr.Textbox(label="Paste text manually", lines=10) | |
| with gr.Row(): | |
| typed_question = gr.Textbox(label="Type your question") | |
| audio_input = gr.Audio(source="microphone",type="filepath", label="Or speak your question") | |
| qa_btn = gr.Button("Get Answer") | |
| qa_output = gr.Textbox(label="Answer and Confidence", lines=3) | |
| def handle_qa(file, text, typed, audio): | |
| context = "" | |
| if file: | |
| context = extract_text(file) | |
| elif text: | |
| context = text | |
| else: | |
| return "❗ Please upload or paste content." | |
| if typed: | |
| question = typed | |
| elif audio: | |
| question = transcribe(audio) | |
| else: | |
| return "❗ Please speak or type a question." | |
| return ask_question(question, context) | |
| qa_btn.click(handle_qa, inputs=[uploaded_file, pasted_text, typed_question, audio_input], outputs=qa_output) | |
| with gr.Tab("Summarization"): | |
| with gr.Row(): | |
| sum_file = gr.File(label="Upload .pdf / .docx / .txt", file_types=[".pdf", ".docx", ".txt"]) | |
| sum_text = gr.Textbox(label="Or paste content", lines=10) | |
| sum_btn = gr.Button("Summarize") | |
| sum_output = gr.Textbox(label="Summary", lines=4) | |
| def handle_summary(file, text): | |
| if file: | |
| context = extract_text(file) | |
| elif text: | |
| context = text | |
| else: | |
| return "❗ Please upload or paste content to summarize." | |
| return summarize_text(context) | |
| sum_btn.click(handle_summary, inputs=[sum_file, sum_text], outputs=sum_output) | |
| demo.launch() | |