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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()
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