Update app.py

app.py

@@ -11,18 +11,20 @@ from difflib import SequenceMatcher
 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)
+# Extract answer for comparison
 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 ""
 
+# Generate questions from text
 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
+    return (questions, text, 0)  # This is stored in conversation_state
 
+# Play the next question
 def ask_question(state):
     questions, context, idx = state
     if idx >= len(questions):
@@ -35,10 +37,11 @@ def ask_question(state):
 
     return question, audio_path, (questions, context, idx + 1)
 
+# Transcribe and provide feedback
 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
+        return "❗ Please ask a question first.", state
 
     recognizer = sr.Recognizer()
     with sr.AudioFile(audio_path) as source:
@@ -49,10 +52,9 @@ def transcribe_and_feedback(audio_path, state):
             return "❌ Could not understand your answer.", state
 
     # Compare with expected answer
-    question = questions[idx - 1]  # subtract 1 because idx was already incremented
+    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:
@@ -60,20 +62,21 @@ def transcribe_and_feedback(audio_path, state):
 
     return feedback, (questions, context, idx)
 
+# Gradio UI
 with gr.Blocks() as app:
-    gr.Markdown("### 🎓 Interactive Speaking Practice with Coursebook Dialogues")
+    gr.Markdown("## 🎓 Interactive Speaking Practice")
 
     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_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")
+    user_audio = gr.Audio(label="🎙️ Your Answer (Record)", sources="microphone", type="filepath")
     transcribe_btn = gr.Button("📝 Submit Answer")
-    feedback_output = gr.Textbox(label="🗨️ Feedback")
+    feedback_output = gr.Textbox(label="💬 Feedback")
 
     conversation_state = gr.State()