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import gradio as gr
import openai
from io import BytesIO
from pydub import AudioSegment
import requests
# Set up OpenAI API
openai.api_key = "YOUR_OPENAI_API_KEY"
def generate_presentation(topic):
prompt = f"Please explain {topic} in the most easy and attractive way possible."
# Set up OpenAI API parameters
model_engine = "text-davinci-002"
max_tokens = 1048
temperature = 0.7
# Generate the presentation content using OpenAI's GPT-3 API
response = openai.Completion.create(
engine=model_engine,
prompt=prompt,
max_tokens=max_tokens,
temperature=temperature
)
return response.choices[0].text
def generate_audio(text):
# Set up text-to-speech API parameters
api_key = "YOUR_TTS_API_KEY"
api_url = "https://api.fpt.ai/hmi/tts/v5"
voice = "banmai"
speed = "0"
# Send a request to the text-to-speech API
headers = {
"api-key": api_key,
"voice": voice,
"speed": speed
}
data = {"text": text}
response = requests.post(api_url, headers=headers, json=data)
# Convert the response audio to a playable format
audio_bytes = BytesIO(response.content)
audio_segment = AudioSegment.from_file(audio_bytes.getvalue(), format="mp3")
audio_segment.export("presentation_audio.mp3", format="mp3")
return audio_bytes
def ai_presentation(topic):
presentation = generate_presentation(topic)
audio = generate_audio(presentation)
# Return the presentation and generated audio
return presentation, audio.read()
# Set up Gradio interface
inputs = gr.inputs.Textbox(label="Enter the topic for your presentation:")
outputs = [
gr.outputs.Textbox(label="Presentation"),
gr.outputs.Audio(label="Presentation Audio", type="audio")
]
gr.Interface(fn=ai_presentation, inputs=inputs, outputs=outputs, title="AICademy",
icon=":books:", server_port=8080).launch()
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