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#app.py
import gradio as gr
import openai
import os
import data6 # Importing the data6 module
import base64
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
openai.api_key = OPENAI_API_KEY
def image_to_base64(img_path):
with open(img_path, "rb") as img_file:
return base64.b64encode(img_file.read()).decode('utf-8')
img_base64 = image_to_base64("SBC6.jpg")
img_html = f'<img src="data:image/jpg;base64,{img_base64}" alt="SBC6" width="300" style="display: block; margin: auto;"/>'
def predict(question_choice, audio):
# Transcribe the audio using Whisper
with open(audio, "rb") as audio_file:
transcript = openai.Audio.transcribe("whisper-1", audio_file)
message = transcript["text"] # This is the transcribed message from the audio input
# Generate the system message based on the chosen question
current_question_index = data6.questions.index(question_choice)
strategy, explanation = data6.strategy_text[current_question_index]
# Construct the conversation with the system and user's message
conversation = [
{
"role": "system",
"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: '{data6.questions[current_question_index]}'. Point out areas they did well and where they can improve. Then, provide a suggested response using the {data6.strategy_text[current_question_index][0]} 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."
},
{"role": "user", "content": message}
]
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
messages=conversation,
temperature=0.7,
max_tokens=500, # Limiting the response to 500 tokens
stream=True
)
partial_message = ""
for chunk in response:
if len(chunk['choices'][0]['delta']) != 0:
partial_message = partial_message + chunk['choices'][0]['delta']['content']
yield partial_message
def get_image_html():
return "" # Markdown syntax to embed the image
# Gradio Interface
iface = gr.Interface(
fn=predict,
inputs=[
gr.Radio(data6.questions, label="Choose a question", default=data6.questions[0]), # Dropdown for question choice
gr.inputs.Audio(source="microphone", type="filepath") # Audio input
],
outputs=gr.inputs.Textbox(), # Using inputs.Textbox as an output to make it editable
description=img_html,
css="custom.css" # Link to the custom CSS file
)
iface.queue().launch()
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