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import gradio as gr
from transformers import pipeline
import torch
from TTS.api import TTS
import tempfile
import os
import speech_recognition as sr
from difflib import SequenceMatcher
# Load models
qg_pipeline = pipeline("text2text-generation", model="valhalla/t5-small-e2e-qg")
tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False, gpu=False)
# Simulate QA by extracting key sentence from input text (placeholder for real QA)
def extract_answer(question, context):
for line in context.split("\n"):
if any(word.lower() in line.lower() for word in question.split()[:3]):
return line
return ""
def generate_questions(text):
output = qg_pipeline(f"generate questions: {text}", num_return_sequences=3)
questions = [q["generated_text"] for q in output]
return questions, text, 0 # store context and index
def ask_question(state):
questions, context, idx = state
if idx >= len(questions):
return "All questions asked.", None, state
question = questions[idx]
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as fp:
tts.tts_to_file(text=question, file_path=fp.name)
audio_path = fp.name
return question, audio_path, (questions, context, idx)
def transcribe_and_feedback(audio_path, state):
questions, context, idx = state
if idx == 0 or idx > len(questions):
return "Please ask a question first.", state
recognizer = sr.Recognizer()
with sr.AudioFile(audio_path) as source:
audio_data = recognizer.record(source)
try:
user_answer = recognizer.recognize_google(audio_data)
except:
return "Could not understand the answer.", state
# Simulate expected answer
question = questions[idx - 1]
expected = extract_answer(question, context)
ratio = SequenceMatcher(None, user_answer.lower(), expected.lower()).ratio()
if ratio > 0.6:
feedback = f"✅ Good answer: {user_answer}"
else:
feedback = f"❌ Try again. You said: {user_answer}"
return feedback, (questions, context, idx)
with gr.Blocks() as app:
gr.Markdown("### 🎓 Interactive Q&A Lesson")
with gr.Row():
course_text = gr.Textbox(lines=8, label="Paste Coursebook Text")
gen_btn = gr.Button("🔄 Generate Questions")
question_text = gr.Textbox(label="Current Question")
question_audio = gr.Audio(label="Listen to Question", type="filepath")
ask_btn = gr.Button("▶️ Ask Next Question")
user_audio = gr.Audio(label="Your Spoken Answer", sources="microphone", type="filepath")
transcribe_btn = gr.Button("📝 Submit Answer")
feedback_output = gr.Textbox(label="Feedback")
conversation_state = gr.State()
gen_btn.click(generate_questions, inputs=course_text, outputs=[conversation_state, course_text, gr.State(0)])
ask_btn.click(ask_question, inputs=conversation_state, outputs=[question_text, question_audio, conversation_state])
transcribe_btn.click(transcribe_and_feedback, inputs=[user_audio, conversation_state], outputs=[feedback_output, conversation_state])
app.launch() |