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import gradio as gr
import torch
from transformers import BertTokenizerFast, BertForQuestionAnswering, pipeline
import torch.nn.functional as F
import speech_recognition as sr
# Load QA Model
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
model = BertForQuestionAnswering.from_pretrained("bert-base-uncased")
model.load_state_dict(torch.load("lexpilot_trained_model.pt", map_location=torch.device("cpu")))
model.eval()
# Load Summarizer
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
def answer_with_confidence(question, context):
inputs = tokenizer.encode_plus(question, context, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
start_logits = outputs.start_logits
end_logits = outputs.end_logits
start_probs = F.softmax(start_logits, dim=1)
end_probs = F.softmax(end_logits, dim=1)
start = torch.argmax(start_probs)
end = torch.argmax(end_probs) + 1
confidence = (start_probs[0][start] * end_probs[0][end - 1]).item()
tokens = inputs["input_ids"][0][start:end]
answer = tokenizer.decode(tokens)
return answer.strip(), round(confidence, 2)
def summarize_text(text):
summary = summarizer(text, max_length=100, min_length=30, do_sample=False)
return summary[0]["summary_text"]
def voice_to_text(audio):
recognizer = sr.Recognizer()
with sr.AudioFile(audio) as source:
audio_data = recognizer.record(source)
try:
return recognizer.recognize_google(audio_data)
except Exception as e:
return f"Error: {str(e)}"
def qa_interface(context, question):
answer, confidence = answer_with_confidence(question, context)
summary = summarize_text(context)
return answer, confidence, summary
with gr.Blocks() as app:
gr.Markdown("## 🤖 LexPilot: QA + Summarization + Voice Input")
with gr.Row():
context_input = gr.Textbox(lines=6, label="📘 Context")
question_input = gr.Textbox(lines=1, label="❓ Question")
with gr.Row():
answer_output = gr.Textbox(label="🧠 Answer")
confidence_output = gr.Number(label="📊 Confidence")
summary_output = gr.Textbox(label="📝 Summary")
with gr.Row():
submit_btn = gr.Button("🔍 Get Answer & Summary")
audio_input = gr.Audio(type="filepath", label="🎙️ Upload Voice Question")
submit_btn.click(fn=qa_interface, inputs=[context_input, question_input],
outputs=[answer_output, confidence_output, summary_output])
audio_input.change(fn=voice_to_text, inputs=audio_input, outputs=question_input)
app.launch()
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