Update app.py

app.py

@@ -12,43 +12,64 @@ qg_pipeline = pipeline("text2text-generation", model="valhalla/t5-small-e2e-qg")
 # Load TTS model
 tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False, gpu=False)
 
+# Global storage
+last_answer = ""
+
 def generate_question(text):
+    global last_answer
+
+    # Extract a possible answer from the text (you can improve this logic)
+    last_answer = "They are Aladdin lamps"  # You can auto-extract later
+
     # Prompt for question generation
     input_text = f"generate question: {text.strip()}"
-    question = qg_pipeline(input_text)[0]["generated_text"]
+    generated = qg_pipeline(input_text, max_length=64)[0]["generated_text"]
 
-    # Save spoken question as audio
+    # Keep only the first question
+    question = generated.split("<sep>")[0].strip()
+
+    # Generate audio
     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
 
-def transcribe_audio(audio_path):
+def transcribe_and_check(audio_path):
+    global last_answer
     recognizer = sr.Recognizer()
     with sr.AudioFile(audio_path) as source:
         audio_data = recognizer.record(source)
         try:
-            return recognizer.recognize_google(audio_data)
+            response = recognizer.recognize_google(audio_data)
         except sr.UnknownValueError:
-            return "Sorry, I could not understand your answer."
+            return "Sorry, I could not understand your answer.", ""
         except sr.RequestError:
-            return "Sorry, there was an error with the speech recognition service."
+            return "Speech recognition service error.", ""
+
+    # Check match (very basic – can be made smarter)
+    if last_answer.lower() in response.lower():
+        feedback = "✔️ Correct!"
+    else:
+        feedback = "❌ Try again."
+
+    return response, feedback
 
 with gr.Blocks() as app:
-    gr.Markdown("### 📘 Enter your coursebook text below:")
+    gr.Markdown("### 🎓 Interactive Coursebook Q&A")
 
-    course_text = gr.Textbox(lines=6, label="Coursebook Text")
+    course_text = gr.Textbox(lines=6, label="📘 Coursebook Text")
 
-    generate_btn = gr.Button("🎤 Generate Question and Speak")
-    question_output = gr.Textbox(label="Generated Question")
-    audio_output = gr.Audio(label="Question Audio", type="filepath")
+    generate_btn = gr.Button("🔊 Generate Question and Speak")
+    question_output = gr.Textbox(label="🧠 Generated Question")
+    audio_output = gr.Audio(label="🔈 Question Audio", type="filepath")
 
-    user_audio = gr.Audio(label="Your Answer", type="filepath", sources=["microphone"])
+    user_audio = gr.Audio(label="🎤 Your Answer", type="filepath", sources=["microphone"])
     transcribe_btn = gr.Button("📝 Transcribe Answer")
-    transcription_output = gr.Textbox(label="Transcribed Answer")
+    transcription_output = gr.Textbox(label="🗣 Transcribed Answer")
+    feedback_output = gr.Textbox(label="🧪 Feedback")
 
     generate_btn.click(fn=generate_question, inputs=course_text, outputs=[question_output, audio_output])
-    transcribe_btn.click(fn=transcribe_audio, inputs=user_audio, outputs=transcription_output)
+    transcribe_btn.click(fn=transcribe_and_check, inputs=user_audio, outputs=[transcription_output, feedback_output])
 
 app.launch()