Update app.py
Browse files
app.py
CHANGED
@@ -21,19 +21,17 @@ def predict(question_choice, audio):
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message = transcript["text"] # This is the transcribed message from the audio input
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# Generate the system message based on the chosen question
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strategy, explanation = HongWenData.strategy_text[current_question_index]
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# Construct the conversation with the system and user's message
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conversation = [
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{
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"role": "system",
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"content": f"You are an expert English Language Teacher in a Singapore Primary school, directly guiding a Primary 6 student in Singapore. The student is answering the question: '{
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},
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{"role": "user", "content": message}
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]
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response = openai.ChatCompletion.create(
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model='gpt-3.5-turbo',
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messages=conversation,
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@@ -48,11 +46,6 @@ def predict(question_choice, audio):
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partial_message = partial_message + chunk['choices'][0]['delta']['content']
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yield partial_message
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def get_image_html():
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return "" # Markdown syntax to embed the image
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# Gradio Interface
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iface = gr.Interface(
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fn=predict,
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@@ -66,3 +59,4 @@ iface = gr.Interface(
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)
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iface.queue().launch()
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message = transcript["text"] # This is the transcribed message from the audio input
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# Generate the system message based on the chosen question
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strategy, explanation = HongWenData.strategy_text["TREES"]
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# Construct the conversation with the system and user's message
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conversation = [
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{
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"role": "system",
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"content": f"You are an expert English Language Teacher in a Singapore Primary school, directly guiding a Primary 6 student in Singapore. The student is answering the question: '{question_choice}'. Point out areas they did well and where they can improve. Then, provide a suggested response using the {strategy} strategy. Encourage the use of sophisticated vocabulary and expressions. For the second and third questions, the picture is not relevant, so the student should not refer to it in their response. {explanation} The feedback should be in second person, addressing the student directly."
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},
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{"role": "user", "content": message}
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]
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response = openai.ChatCompletion.create(
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model='gpt-3.5-turbo',
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messages=conversation,
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partial_message = partial_message + chunk['choices'][0]['delta']['content']
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yield partial_message
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# Gradio Interface
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iface = gr.Interface(
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fn=predict,
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)
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iface.queue().launch()
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