Update app.py

app.py

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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)
+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)  # this tuple is stored in state
+
+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 + 1)
+
+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 your answer.", state
+
+    # Compare with expected answer
+    question = questions[idx - 1]  # subtract 1 because idx was already incremented
+    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 Speaking Practice with Coursebook Dialogues")
+
+    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(fn=generate_questions, inputs=course_text, outputs=conversation_state)
+    ask_btn.click(fn=ask_question, inputs=conversation_state, outputs=[question_text, question_audio, conversation_state])
+    transcribe_btn.click(fn=transcribe_and_feedback, inputs=[user_audio, conversation_state], outputs=[feedback_output, conversation_state])
+
+app.launch()