simonraj commited on
Commit
fdcdf8e
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verified ·
1 Parent(s): 9ed052b

Update app.py

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Files changed (1) hide show
  1. app.py +3 -13
app.py CHANGED
@@ -47,7 +47,6 @@ def transcribe(audio_path):
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  text = model.stt_file(audio_file)[0]
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  return text
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- # Inference function using Hugging Face InferenceClient
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  # Inference function using Hugging Face InferenceClient
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  @spaces.GPU(duration=120)
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  def model(text):
@@ -63,7 +62,7 @@ def model(text):
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  do_sample=True,
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  seed=42,
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  )
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- formatted_prompt = system_instructions + text + "[CrucialCoach]"
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  stream = client.text_generation(
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  formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
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  output = ""
@@ -80,7 +79,7 @@ async def generate_audio_feedback(feedback_text):
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  await communicate.save(tmp_path)
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  return tmp_path
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- #generate feedback
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  async def generate_feedback(user_id, question_choice, strategy_choice, message, feedback_level):
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  current_question_index = thinkingframes.questions.index(question_choice)
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  strategy, explanation = thinkingframes.strategy_options[strategy_choice]
@@ -174,15 +173,6 @@ async def predict(question_choice, strategy_choice, feedback_level, audio):
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  chat_history.append(("Oral Coach ⚡ ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", "Transcription complete. Generating feedback. Please continue listening to your oral response while waiting ..."))
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  yield chat_history, current_audio_output
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- moderation_response = client.moderations.create(input=student_response)
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- flagged = any(result.flagged for result in moderation_response.results)
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- if flagged:
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- moderated_message = "The message has been flagged. Please see your teacher to clarify."
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- questionNo = thinkingframes.questions.index(question_choice) + 1
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- add_submission(int(user_state.value), moderated_message, "", int(0), "", questionNo)
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- yield chat_history, current_audio_output
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- return
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-
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  accumulated_feedback = "" # Variable to store the accumulated feedback
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  async for feedback_chunk in generate_feedback(int(user_state.value), question_choice, strategy_choice, student_response, feedback_level):
@@ -268,4 +258,4 @@ with gr.Blocks(title="Oral Coach powered by ZeroGPU⚡ϞϞ(๑⚈ ․̫ ⚈๑)
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  create_teachers_dashboard_tab()
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  demo.queue(max_size=20)
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- demo.launch()
 
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  text = model.stt_file(audio_file)[0]
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  return text
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  # Inference function using Hugging Face InferenceClient
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  @spaces.GPU(duration=120)
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  def model(text):
 
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  do_sample=True,
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  seed=42,
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  )
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+ formatted_prompt = system_instructions + text + "[OralCoach]"
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  stream = client.text_generation(
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  formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
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  output = ""
 
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  await communicate.save(tmp_path)
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  return tmp_path
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+ # Generating feedback for the Oral Coach
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  async def generate_feedback(user_id, question_choice, strategy_choice, message, feedback_level):
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  current_question_index = thinkingframes.questions.index(question_choice)
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  strategy, explanation = thinkingframes.strategy_options[strategy_choice]
 
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  chat_history.append(("Oral Coach ⚡ ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", "Transcription complete. Generating feedback. Please continue listening to your oral response while waiting ..."))
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  yield chat_history, current_audio_output
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  accumulated_feedback = "" # Variable to store the accumulated feedback
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  async for feedback_chunk in generate_feedback(int(user_state.value), question_choice, strategy_choice, student_response, feedback_level):
 
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  create_teachers_dashboard_tab()
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  demo.queue(max_size=20)
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+ demo.launch(share=False)