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import gradio as gr
import base64
import os
from openai import OpenAI
import json
import fitz
from PIL import Image
import io
from settings_mgr import generate_download_settings_js, generate_upload_settings_js
from chat_export import import_history, get_export_js
from doc2json import process_docx
from code_exec import eval_restricted_script
dump_controls = False
log_to_console = False
temp_files = []
def encode_image(image_data):
"""Generates a prefix for image base64 data in the required format for the
four known image formats: png, jpeg, gif, and webp.
Args:
image_data: The image data, encoded in base64.
Returns:
A string containing the prefix.
"""
# Get the first few bytes of the image data.
magic_number = image_data[:4]
# Check the magic number to determine the image type.
if magic_number.startswith(b'\x89PNG'):
image_type = 'png'
elif magic_number.startswith(b'\xFF\xD8'):
image_type = 'jpeg'
elif magic_number.startswith(b'GIF89a'):
image_type = 'gif'
elif magic_number.startswith(b'RIFF'):
if image_data[8:12] == b'WEBP':
image_type = 'webp'
else:
# Unknown image type.
raise Exception("Unknown image type")
else:
# Unknown image type.
raise Exception("Unknown image type")
return f"data:image/{image_type};base64,{base64.b64encode(image_data).decode('utf-8')}"
def process_pdf_img(pdf_fn: str):
pdf = fitz.open(pdf_fn)
message_parts = []
for page in pdf.pages():
# Create a transformation matrix for rendering at the calculated scale
mat = fitz.Matrix(0.6, 0.6)
# Render the page to a pixmap
pix = page.get_pixmap(matrix=mat, alpha=False)
# Convert pixmap to PIL Image
img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
# Convert PIL Image to bytes
img_byte_arr = io.BytesIO()
img.save(img_byte_arr, format='PNG')
img_byte_arr = img_byte_arr.getvalue()
# Encode image to base64
base64_encoded = base64.b64encode(img_byte_arr).decode('utf-8')
# Construct the data URL
image_url = f"data:image/png;base64,{base64_encoded}"
# Append the message part
message_parts.append({
"type": "text",
"text": f"Page {page.number} of file '{pdf_fn}'"
})
message_parts.append({
"type": "image_url",
"image_url": {
"url": image_url,
"detail": "high"
}
})
pdf.close()
return message_parts
def encode_file(fn: str) -> list:
user_msg_parts = []
if fn.endswith(".docx"):
user_msg_parts.append({"type": "text", "text": process_docx(fn)})
elif fn.endswith(".pdf"):
user_msg_parts.extend(process_pdf_img(fn))
else:
with open(fn, mode="rb") as f:
content = f.read()
isImage = False
if isinstance(content, bytes):
try:
# try to add as image
content = encode_image(content)
isImage = True
except:
# not an image, try text
content = content.decode('utf-8', 'replace')
else:
content = str(content)
if isImage:
user_msg_parts.append({"type": "image_url",
"image_url":{"url": content}})
else:
fn = os.path.basename(fn)
user_msg_parts.append({"type": "text", "text": f"```{fn}\n{content}\n```"})
return user_msg_parts
def undo(history):
history.pop()
return history
def dump(history):
return str(history)
def load_settings():
# Dummy Python function, actual loading is done in JS
pass
def save_settings(acc, sec, prompt, temp, tokens, model):
# Dummy Python function, actual saving is done in JS
pass
def process_values_js():
return """
() => {
return ["oai_key", "system_prompt", "seed"];
}
"""
def bot(message, history, oai_key, system_prompt, seed, temperature, max_tokens, model, python_use):
try:
client = OpenAI(
api_key=oai_key
)
if model == "whisper":
result = ""
whisper_prompt = system_prompt
for msg in history:
content = msg["content"]
if msg["role"] == "user":
if type(content) is tuple:
pass
else:
whisper_prompt += f"\n{content}"
if msg["role"] == "assistant":
whisper_prompt += f"\n{content}"
if message["text"]:
whisper_prompt += message["text"]
if message.files:
for file in message.files:
audio_fn = os.path.basename(file.path)
with open(file.path, "rb") as f:
transcription = client.audio.transcriptions.create(
model="whisper-1",
prompt=whisper_prompt,
file=f,
response_format="text"
)
whisper_prompt += f"\n{transcription}"
result += f"\n``` transcript {audio_fn}\n {transcription}\n```"
yield result
elif model == "dall-e-3":
response = client.images.generate(
model=model,
prompt=message["text"],
size="1792x1024",
quality="hd",
n=1,
)
yield gr.Image(response.data[0].url)
else:
seed_i = None
if seed:
seed_i = int(seed)
tools = None if not python_use else [
{
"type": "function",
"function": {
"name": "eval_python",
"description": "Evaluate a simple script written in a conservative, restricted subset of Python."
"Note: Augmented assignments, in-place operations (e.g., +=, -=), lambdas (e.g. list comprehensions) are not supported. "
"Use regular assignments and operations instead. Only 'import math' is allowed. "
"Returns: unquoted results without HTML encoding.",
"parameters": {
"type": "object",
"properties": {
"python_source_code": {
"type": "string",
"description": "The Python script that will run in a RestrictedPython context. "
"Avoid using augmented assignments or in-place operations (+=, -=, etc.), as well as lambdas (e.g. list comprehensions). "
"Use regular assignments and operations instead. Only 'import math' is allowed. Results need to be reported through print()."
}
},
"required": ["python_source_code"]
}
}
}
]
if log_to_console:
print(f"bot history: {str(history)}")
history_openai_format = []
user_msg_parts = []
if system_prompt:
if not model.startswith("o"):
role = "system"
else:
role = "developer"
if not system_prompt.startswith("Formatting re-enabled"):
system_prompt = "Formatting re-enabled\n" + system_prompt
history_openai_format.append({"role": role, "content": system_prompt})
for msg in history:
role = msg["role"]
content = msg["content"]
if role == "user":
if isinstance(content, gr.File) or isinstance(content, gr.Image):
user_msg_parts.extend(encode_file(content.value['path']))
elif isinstance(content, tuple):
user_msg_parts.extend(encode_file(content[0]))
else:
user_msg_parts.append({"type": "text", "text": content})
if role == "assistant":
if user_msg_parts:
history_openai_format.append({"role": "user", "content": user_msg_parts})
user_msg_parts = []
history_openai_format.append({"role": "assistant", "content": content})
if message["text"]:
user_msg_parts.append({"type": "text", "text": message["text"]})
if message["files"]:
for file in message["files"]:
user_msg_parts.extend(encode_file(file))
history_openai_format.append({"role": "user", "content": user_msg_parts})
user_msg_parts = []
if log_to_console:
print(f"br_prompt: {str(history_openai_format)}")
if model in ["o1", "o1-high", "o1-2024-12-17", "o3-mini", "o3-mini-high"]:
# reasoning effort
high = False
if model == "o1-high":
model = "o1"
high = True
elif model == "o3-mini-high":
model = "o3-mini"
high = True
response = client.chat.completions.create(
model=model,
messages= history_openai_format,
seed=seed_i,
reasoning_effort="high" if high else "medium",
**({"max_completion_tokens": max_tokens} if max_tokens > 0 else {})
)
yield response.choices[0].message.content
if log_to_console:
print(f"usage: {response.usage}")
else:
whole_response = ""
while True:
response = client.chat.completions.create(
model=model,
messages= history_openai_format,
temperature=temperature,
seed=seed_i,
max_tokens=max_tokens,
stream=True,
stream_options={"include_usage": True},
**{"tools": tools} if python_use else {},
tool_choice = "auto" if python_use else None
)
# Accumulators for partial model responses
tool_name_accum = None
tool_args_accum = ""
tool_call_id = None
# process
for chunk in response:
if chunk.choices:
txt = ""
for choice in chunk.choices:
delta = choice.delta
if not delta:
continue
cont = delta.content
if cont:
txt += cont
if delta.tool_calls:
for tc in delta.tool_calls:
if tc.function.name:
tool_name_accum = tc.function.name
if tc.function.arguments:
tool_args_accum += tc.function.arguments
if tc.id:
tool_call_id = tc.id
finish_reason = choice.finish_reason
if finish_reason:
if finish_reason == "tool_calls":
try:
parsed_args = json.loads(tool_args_accum)
tool_script = parsed_args.get("python_source_code", "")
whole_response += f"\n``` script\n{tool_script}\n```\n"
yield whole_response
tool_result = eval_restricted_script(tool_script)
whole_response += f"\n``` result\n{tool_result if not tool_result['success'] else tool_result['prints']}\n```\n"
yield whole_response
history_openai_format.extend([
{
"role": "assistant",
"content": txt,
"tool_calls": [
{
"id": tool_call_id,
"type": "function",
"function": {
"name": tool_name_accum,
"arguments": json.dumps(parsed_args)
}
}
]
},
{
"role": "tool",
"tool_call_id": tool_call_id,
"name": tool_name_accum,
"content": json.dumps(tool_result)
}
])
except Exception as e:
history_openai_format.extend([{
"role": "tool",
"tool_call_id": tool_call_id,
"name": tool_name_accum,
"content": [
{
"toolResult": {
"content": [{"text": e.args[0]}],
"status": 'error'
}
}
]
}])
whole_response += f"\n``` error\n{e.args[0]}\n```\n"
yield whole_response
else:
return
else:
whole_response += txt
yield whole_response
if chunk.usage and log_to_console:
print(f"usage: {chunk.usage}")
if log_to_console:
print(f"br_result: {str(history)}")
except Exception as e:
raise gr.Error(f"Error: {str(e)}")
def import_history_guarded(oai_key, history, file):
# check credentials first
try:
client = OpenAI(api_key=oai_key)
client.models.retrieve("gpt-4o")
except Exception as e:
raise gr.Error(f"OpenAI login error: {str(e)}")
# actual import
return import_history(history, file)
with gr.Blocks(delete_cache=(86400, 86400)) as demo:
gr.Markdown("# OAI Chat (Nils' Version™️)")
with gr.Accordion("Startup"):
gr.Markdown("""Use of this interface permitted under the terms and conditions of the
[MIT license](https://github.com/ndurner/oai_chat/blob/main/LICENSE).
Third party terms and conditions apply, particularly
those of the LLM vendor (OpenAI) and hosting provider (Hugging Face). This app and the AI models may make mistakes, so verify any outputs.""")
oai_key = gr.Textbox(label="OpenAI API Key", elem_id="oai_key")
model = gr.Dropdown(label="Model", value="gpt-4-turbo", allow_custom_value=True, elem_id="model",
choices=["gpt-4o", "gpt-4-turbo", "o1-high", "o1-mini", "o1", "o3-mini-high", "o3-mini", "o1-preview", "chatgpt-4o-latest", "gpt-4o-2024-05-13", "gpt-4o-2024-11-20", "gpt-4o-mini", "gpt-4", "gpt-4-vision-preview", "gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-1106", "whisper", "dall-e-3"])
system_prompt = gr.TextArea("You are a helpful yet diligent AI assistant. Answer faithfully and factually correct. Respond with 'I do not know' if uncertain.", label="System/Developer Prompt", lines=3, max_lines=250, elem_id="system_prompt")
seed = gr.Textbox(label="Seed", elem_id="seed")
temp = gr.Slider(0, 2, label="Temperature", elem_id="temp", value=1)
max_tokens = gr.Slider(0, 16384, label="Max. Tokens", elem_id="max_tokens", value=800)
python_use = gr.Checkbox(label="Python Use", value=False)
save_button = gr.Button("Save Settings")
load_button = gr.Button("Load Settings")
dl_settings_button = gr.Button("Download Settings")
ul_settings_button = gr.Button("Upload Settings")
load_button.click(load_settings, js="""
() => {
let elems = ['#oai_key textarea', '#system_prompt textarea', '#seed textarea', '#temp input', '#max_tokens input', '#model'];
elems.forEach(elem => {
let item = document.querySelector(elem);
let event = new InputEvent('input', { bubbles: true });
item.value = localStorage.getItem(elem.split(" ")[0].slice(1)) || '';
item.dispatchEvent(event);
});
}
""")
save_button.click(save_settings, [oai_key, system_prompt, seed, temp, max_tokens, model], js="""
(oai, sys, seed, temp, ntok, model) => {
localStorage.setItem('oai_key', oai);
localStorage.setItem('system_prompt', sys);
localStorage.setItem('seed', seed);
localStorage.setItem('temp', document.querySelector('#temp input').value);
localStorage.setItem('max_tokens', document.querySelector('#max_tokens input').value);
localStorage.setItem('model', model);
}
""")
control_ids = [('oai_key', '#oai_key textarea'),
('system_prompt', '#system_prompt textarea'),
('seed', '#seed textarea'),
('temp', '#temp input'),
('max_tokens', '#max_tokens input'),
('model', '#model')]
controls = [oai_key, system_prompt, seed, temp, max_tokens, model, python_use]
dl_settings_button.click(None, controls, js=generate_download_settings_js("oai_chat_settings.bin", control_ids))
ul_settings_button.click(None, None, None, js=generate_upload_settings_js(control_ids))
chat = gr.ChatInterface(fn=bot, multimodal=True, additional_inputs=controls, autofocus = False, type = "messages")
chat.textbox.file_count = "multiple"
chat.textbox.max_plain_text_length = 2**31
chatbot = chat.chatbot
chatbot.show_copy_button = True
chatbot.height = 450
if dump_controls:
with gr.Row():
dmp_btn = gr.Button("Dump")
txt_dmp = gr.Textbox("Dump")
dmp_btn.click(dump, inputs=[chatbot], outputs=[txt_dmp])
with gr.Accordion("Import/Export", open = False):
import_button = gr.UploadButton("History Import")
export_button = gr.Button("History Export")
export_button.click(lambda: None, [chatbot, system_prompt], js=get_export_js())
dl_button = gr.Button("File download")
dl_button.click(lambda: None, [chatbot], js="""
(chat_history) => {
const languageToExt = {
'python': 'py',
'javascript': 'js',
'typescript': 'ts',
'csharp': 'cs',
'ruby': 'rb',
'shell': 'sh',
'bash': 'sh',
'markdown': 'md',
'yaml': 'yml',
'rust': 'rs',
'golang': 'go',
'kotlin': 'kt'
};
const contentRegex = /```(?:([^\\n]+)?\\n)?([\\s\\S]*?)```/;
const match = contentRegex.exec(chat_history[chat_history.length - 1][1]);
if (match && match[2]) {
const specifier = match[1] ? match[1].trim() : '';
const content = match[2];
let filename = 'download';
let fileExtension = 'txt'; // default
if (specifier) {
if (specifier.includes('.')) {
// If specifier contains a dot, treat it as a filename
const parts = specifier.split('.');
filename = parts[0];
fileExtension = parts[1];
} else {
// Use mapping if exists, otherwise use specifier itself
const langLower = specifier.toLowerCase();
fileExtension = languageToExt[langLower] || langLower;
filename = 'code';
}
}
const blob = new Blob([content], {type: 'text/plain'});
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url;
a.download = `${filename}.${fileExtension}`;
document.body.appendChild(a);
a.click();
document.body.removeChild(a);
URL.revokeObjectURL(url);
}
}
""")
import_button.upload(import_history_guarded,
inputs=[oai_key, chatbot, import_button],
outputs=[chatbot, system_prompt])
demo.unload(lambda: [os.remove(file) for file in temp_files])
demo.queue(default_concurrency_limit = None).launch() |