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main.py
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main.py
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import torchaudio
import torch
import os
import time
import soundfile as sf
import json
import os
import asyncio
import streamlit as st
from src.module.semantic_kernel_module import SemanticKernelDataModule
from src.module.taskweaver_module import TaskWeaverDataProcessor
from src.module.autogen_module import AutoGenModule
def process_image(image, project_id, region, access_token):
buffered = io.BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue()).decode()
request_body = {
"contents": {
"role": "user",
"parts": [
{
"fileData": {
"mimeType": "image/jpeg",
"data": img_str
}
},
{
"text": "Describe this picture from the perspective of SoW on the topic of the picture. use titles and subtitles to produce a complete statement of work based on the picture provided."
}
]
},
"safety_settings": {
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"threshold": "BLOCK_LOW_AND_ABOVE"
},
"generation_config": {
"temperature": 0.4,
"topP": 1.0,
"topK": 32,
"maxOutputTokens": 2048
}
}
endpoint = f"https://{region}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{region}/publishers/google/models/gemini-pro-vision:streamGenerateContent"
response = requests.post(endpoint, headers={"Authorization": f"Bearer {access_token}"}, json=request_body)
return response.json()
def save_and_resample_audio(input_audio_path, output_audio_path, resample_rate=16000):
waveform, sample_rate = torchaudio.load(input_audio_path)
resampler = torchaudio.transforms.Resample(sample_rate, resample_rate, dtype=waveform.dtype)
resampled_waveform = resampler(waveform)
torchaudio.save(output_audio_path, resampled_waveform, resample_rate)
def save_audio(audio_input, output_dir="saved_audio", resample_rate=16000):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
sample_rate, audio_data = audio_input
file_name = f"audio_{int(time.time())}.wav"
file_path = os.path.join(output_dir, file_name)
sf.write(file_path, audio_data, sample_rate)
resampled_file_path = os.path.join(output_dir, f"resampled_{file_name}")
save_and_resample_audio(file_path, resampled_file_path, resample_rate)
return resampled_file_path
def speech_to_text(audio_data, tgt_lang):
file_path = save_audio(audio_data)
audio_input, _ = torchaudio.load(file_path)
s2t_model = torch.jit.load("unity_on_device.ptl", map_location=torch.device('cpu'))
with torch.no_grad():
model_output = s2t_model(audio_input, tgt_lang=languages[tgt_lang])
transcribed_text = model_output[0] if model_output else ""
print("Speech to Text Model Output:", transcribed_text)
return transcribed_text
def speech_to_speech_translation(audio_data, tgt_lang):
file_path = save_audio(audio_data)
audio_input, _ = torchaudio.load(file_path)
s2st_model = torch.jit.load("unity_on_device.ptl", map_location=torch.device('cpu'))
with torch.no_grad():
translated_text, units, waveform = s2st_model(audio_input, tgt_lang=languages[tgt_lang])
output_file = "/tmp/result.wav"
torchaudio.save(output_file, waveform.unsqueeze(0), sample_rate=16000)
print("Translated Text:", translated_text)
print("Units:", units)
print("Waveform Shape:", waveform.shape)
return translated_text, output_file
async def process_user_input(user_input):
semantic_kernel = SemanticKernelDataModule()
taskweaver = TaskWeaverDataProcessor()
autogen_module = AutoGenModule(memgpt_memory_path="./src/autogen/MemGPT", openai_api_key=os.getenv('OPENAI_API_KEY'))
autogen_module.semantic_kernel = semantic_kernel
autogen_module.taskweaver = taskweaver
sow_document = await semantic_kernel.create_and_fetch_sow(user_input)
executed_plan = await autogen_module.AutoGenModule(sow_document)
return executed_plan
async def process_user_input(user_input):
semantic_kernel = SemanticKernelDataModule()
taskweaver = TaskWeaverDataProcessor()
autogen_module = AutoGenModule(memgpt_memory_path="./src/autogen/MemGPT", openai_api_key=os.getenv('OPENAI_API_KEY'))
autogen_module.semantic_kernel = semantic_kernel
autogen_module.taskweaver = taskweaver
sow_document = await semantic_kernel.create_and_fetch_sow(user_input)
executed_plan = await autogen_module.AutoGenModule(sow_document)
return executed_plan
# @cl.on_chat_start
# async def start():
# # Initial setup if needed
# pass
# @cl.on_message
# async def main(message: cl.Message):
# user_input = message.content
# executed_plan = await process_user_input(user_input)
# # Assuming the executed_plan is a string or something that can be converted to a string
# await cl.Message(content=str(executed_plan)).send()
# # Streamlit app
# def main():
# st.title("DataTonic")
# st.subheader("馃専DataTonic: A Data-Capable AGI-style Agent Builder of Agents")
# # User input text
# user_input = st.text_area("Describe your request in detail with objectives and how you might achieve it:")
# # Language selection for speech-to-text
# languages = {
# "English": "eng",
# "Hindi": "hin",
# "Portuguese": "por",
# "Russian": "rus",
# "Spanish": "spa"
# }
# selected_language = st.selectbox("Select your native language to speak to DataTonic", list(languages.keys()))
# # Audio recording
# st.write("Or record your voice:")
# audio = mic_recorder(start_prompt="鈴猴笍", stop_prompt="鈴癸笍", key='recorder')
# # Process text or audio input
# if st.button("Process"):
# if user_input:
# result = asyncio.run(process_user_input(user_input))
# st.write("Processed Result:", result)
# elif audio:
# # Process the audio input
# audio_data = (audio['sample_rate'], audio['bytes'])
# transcribed_text = speech_to_text(audio_data, languages[selected_language])
# st.write("Transcribed Text:", transcribed_text)
# # Optionally, send the transcribed text for further processing
# result = asyncio.run(process_user_input(transcribed_text))
# st.write("Processed Result:", result)
# if __name__ == "__main__":
# main()
def main():
st.title("DataTonic")
st.subheader("馃専DataTonic: A Data-Capable AGI-style Agent Builder of Agents")
# Input fields for project ID, region, and access token
project_id = st.text_input("Enter your Vertex/Gemini project ID")
region = st.text_input("Enter your Gemini Project region")
access_token = st.text_input("Enter your Google API access token", type="password")
# User input text
user_input = st.text_area("Describe your request in detail with objectives and how you might achieve it:")
# Language selection for speech-to-text
languages = {
"English": "eng",
"Hindi": "hin",
"Portuguese": "por",
"Russian": "rus",
"Spanish": "spa"
}
selected_language = st.selectbox("Select your native language to speak to DataTonic", list(languages.keys()))
# Audio recording
st.write("Or record your voice:")
audio = mic_recorder(start_prompt="鈴猴笍", stop_prompt="鈴癸笍", key='recorder')
# Image upload
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
# Process text, audio, or image input
if st.button("Process"):
combined_input = user_input
if audio:
# Process the audio input
audio_data = (audio['sample_rate'], audio['bytes'])
transcribed_text = speech_to_text(audio_data, languages[selected_language])
st.write("Transcribed Text:", transcribed_text)
combined_input += "\n" + transcribed_text
if uploaded_image:
# Process the uploaded image
image_result = process_image(uploaded_image, project_id, region, access_token)
st.write("Image Processing Result:", image_result)
combined_input += "\n[Image Processed]"
if combined_input:
result = asyncio.run(process_user_input(combined_input))
st.write("Processed Result:", result)
if __name__ == "__main__":
main()