Multimodal Integration with OpenAI
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In this demo, you’ll create a multimodal language tutor app using Gradio. The app will simulate conversational scenarios, allowing users to practice their English skills interactively. The app will display images, play audio prompts, and let users respond via recorded speech. It will then update the conversation, generate new images, and provide audio feedback based on the user’s input.
# Build the multimodal language tutor app using Gradio
# Initial seed prompt for generating the initial situational context
seed_prompt = "cafe near beach" # or "comics exhibition",
"meeting parents-in-law for the first time", etc
# Generate an initial situational description based on the seed prompt
initial_situation = generate_situational_prompt(seed_prompt)
# Generate an initial image based on the initial situational description
img = generate_situation_image(initial_situation)
# Flags to manage the state of the app
first_time = True
combined_history = ""
# Function to extract the first and last segments of the conversation
# history
# This is to ensure that the prompt for DALL-E does not exceed the
# maximum character limit of 4000 characters
def extract_first_last(text):
elements = [elem.strip() for elem in text.split('====')
if elem.strip()]
if len(elements) >= 2:
return elements[0] + elements[-1]
elif len(elements) == 1:
return elements[0]
else:
return ""
# Main function to handle the conversation generation logic
def conversation_generation(audio_path):
global combined_history
global first_time
# Transcribe the user's speech from the provided audio file path
transcripted_text = transcript_speech(audio_path)
# Create conversation history based on whether it is the first
# interaction or not
if first_time:
history = creating_conversation_history(initial_situation,
transcripted_text)
first_time = False
else:
history = creating_conversation_history(combined_history,
transcripted_text)
# Generate a new conversation based on the updated history
conversation = generate_conversation_from_history(history)
# Update the combined history with the new conversation
combined_history = history + "\n====\n" + conversation
# Extract a suitable prompt for DALL-E by combining the first
# and last parts of the conversation history
dalle_prompt = extract_first_last(combined_history)
# Generate a new image based on the updated combined history
img = generate_situation_image(combined_history)
# Generate speech for the new conversation and save it to an
# audio file
output_audio_file = "speak_speech.mp3"
speak_prompt(conversation, False, output_audio_file)
# Return the updated image, conversation text, and audio file
# path
return img, conversation, output_audio_file
# Create the Gradio interface for the language tutor app
tutor_app = gr.Interface(
conversation_generation,
gr.Audio(sources=["microphone"], type="filepath"),
outputs=[gr.Image(value=img), gr.Text(), gr.Audio(type="filepath")],
title="Speaking Language Tutor App",
description=initial_situation
)
# Launch the Gradio app
tutor_app.launch()