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import gradio as gr
from mysite.libs.utilities import chat_with_interpreter, completion, process_file,no_process_file
from interpreter import interpreter
import mysite.interpreter.interpreter_config # インポートするだけで設定が適用されます
import duckdb
def format_response(chunk, full_response):
# Message
if chunk["type"] == "message":
full_response += chunk.get("content", "")
if chunk.get("end", False):
full_response += "\n"
# Code
if chunk["type"] == "code":
if chunk.get("start", False):
full_response += "```python\n"
full_response += chunk.get("content", "").replace("`", "")
if chunk.get("end", False):
full_response += "\n```\n"
# Output
if chunk["type"] == "confirmation":
if chunk.get("start", False):
full_response += "```python\n"
full_response += chunk.get("content", {}).get("code", "")
if chunk.get("end", False):
full_response += "```\n"
# Console
if chunk["type"] == "console":
if chunk.get("start", False):
full_response += "```python\n"
if chunk.get("format", "") == "active_line":
console_content = chunk.get("content", "")
if console_content is None:
full_response += "No output available on console."
if chunk.get("format", "") == "output":
console_content = chunk.get("content", "")
full_response += console_content
if chunk.get("end", False):
full_response += "\n```\n"
# Image
if chunk["type"] == "image":
if chunk.get("start", False) or chunk.get("end", False):
full_response += "\n"
else:
image_format = chunk.get("format", "")
if image_format == "base64.png":
image_content = chunk.get("content", "")
if image_content:
image = Image.open(BytesIO(base64.b64decode(image_content)))
new_image = Image.new("RGB", image.size, "white")
new_image.paste(image, mask=image.split()[3])
buffered = BytesIO()
new_image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
full_response += f"\n"
return full_response
# Set the environment variable.
def chat_with_interpreter(
message, history, a=None, b=None, c=None, d=None
): # , openai_api_key):
# Set the API key for the interpreter
# interpreter.llm.api_key = openai_api_key
if message == "reset":
interpreter.messages = []
interpreter.reset()
return "Interpreter reset", history
full_response = ""
# add_conversation(history,20)
user_entry = {"role": "user", "type": "message", "content": message}
#messages.append(user_entry)
# Call interpreter.chat and capture the result
# message = message + "\nシンタックスを確認してください。"
# result = interpreter.chat(message)
for chunk in interpreter.chat(message, display=False, stream=True):
# print(chunk)
# output = '\n'.join(item['content'] for item in result if 'content' in item)
full_response = format_response(chunk, full_response)
yield full_response # chunk.get("content", "")
no_process_file(message,"ai")
# Extract the 'content' field from all elements in the result
yield full_response
return full_response, history
PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
<img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/8e75e61cc9bab22b7ce3dec85ab0e6db1da5d107/Meta_lockup_positive%20primary_RGB.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55; ">
<h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Meta llama3</h1>
<p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
</div>
"""
chatbot = gr.Chatbot(height=650, placeholder=PLACEHOLDER, label="Gradio ChatInterface")
gradio_interface = gr.ChatInterface(
fn=chat_with_interpreter,
chatbot=chatbot,
fill_height=True,
additional_inputs_accordion=gr.Accordion(
label="⚙️ Parameters", open=False, render=False
),
additional_inputs=[
gr.Slider(
minimum=0,
maximum=1,
step=0.1,
value=0.95,
label="Temperature",
render=False,
),
gr.Slider(
minimum=128,
maximum=4096,
step=1,
value=512,
label="Max new tokens",
render=False,
),
],
# democs,
examples=[
["HTMLのサンプルを作成して"],
[
"CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml"
],
],
cache_examples=False,
)
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