import gradio as gr import base64 import os from openai import OpenAI import json from doc2json import process_docx dump_controls = False log_to_console = False # constants image_embed_prefix = "🖼️🆙 " 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 add_text(history, text): history = history + [(text, None)] return history, gr.Textbox(value="", interactive=False) def add_file(history, file): if file.name.endswith(".docx"): content = process_docx(file.name) else: with open(file.name, mode="rb") as f: content = f.read() if isinstance(content, bytes): content = content.decode('utf-8', 'replace') else: content = str(content) fn = os.path.basename(file.name) history = history + [(f'```{fn}\n{content}\n```', None)] gr.Info(f"File added as {fn}") return history def add_img(history, file): if log_to_console: print(f"add_img {file.name}") history = history + [(image_embed_prefix + file.name, None)] gr.Info(f"Image added as {file.name}") return history def submit_text(txt_value): return add_text([chatbot, txt_value], [chatbot, txt_value]) 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): try: client = OpenAI( api_key=oai_key ) seed_i = None if seed: seed_i = int(seed) if log_to_console: print(f"bot history: {str(history)}") history_openai_format = [] user_msg_parts = [] if system_prompt: history_openai_format.append({"role": "system", "content": system_prompt}) for human, assi in history: if human is not None: if human.startswith(image_embed_prefix): with open(human.lstrip(image_embed_prefix), mode="rb") as f: content = f.read() user_msg_parts.append({"type": "image_url", "image_url":{"url": encode_image(content)}}) else: user_msg_parts.append({"type": "text", "text": human}) if assi is not None: if user_msg_parts: history_openai_format.append({"role": "user", "content": user_msg_parts}) user_msg_parts = [] history_openai_format.append({"role": "assistant", "content": assi}) if message: user_msg_parts.append({"type": "text", "text": human}) if user_msg_parts: history_openai_format.append({"role": "user", "content": user_msg_parts}) if log_to_console: print(f"br_prompt: {str(history_openai_format)}") response = client.chat.completions.create( model=model, messages= history_openai_format, temperature=temperature, seed=seed_i, max_tokens=max_tokens ) if log_to_console: print(f"br_response: {str(response)}") history[-1][1] = response.choices[0].message.content if log_to_console: print(f"br_result: {str(history)}") except Exception as e: raise gr.Error(f"Error: {str(e)}") return "", history def import_history(history, file): with open(file.name, mode="rb") as f: content = f.read() if isinstance(content, bytes): content = content.decode('utf-8', 'replace') else: content = str(content) # Deserialize the JSON content to history history = json.loads(content) # The history is returned and will be set to the chatbot component return history with gr.Blocks() as demo: gr.Markdown("# OAI Chat (Nils' Version™️)") with gr.Accordion("Settings"): oai_key = gr.Textbox(label="OpenAI API Key", elem_id="oai_key") model = gr.Dropdown(label="Model", value="gpt-4-1106-preview", allow_custom_value=True, elem_id="model", choices=["gpt-4-1106-preview", "gpt-4", "gpt-4-vision-preview", "gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-1106"]) system_prompt = gr.TextArea("You are a helpful AI.", label="System Prompt", lines=3, max_lines=250, elem_id="system_prompt") seed = gr.Textbox(label="Seed", elem_id="seed") temp = gr.Slider(0, 1, label="Temperature", elem_id="temp", value=1) max_tokens = gr.Slider(1, 4000, label="Max. Tokens", elem_id="max_tokens", value=800) save_button = gr.Button("Save Settings") load_button = gr.Button("Load 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); } """) chatbot = gr.Chatbot( [], elem_id="chatbot", show_copy_button=True, height=350 ) with gr.Row(): btn = gr.UploadButton("📁 Upload", size="sm") img_btn = gr.UploadButton("🖼️ Upload", size="sm", file_types=["image"]) undo_btn = gr.Button("↩️ Undo") undo_btn.click(undo, inputs=[chatbot], outputs=[chatbot]) clear = gr.ClearButton(chatbot, value="🗑️ Clear") with gr.Row(): txt = gr.TextArea( scale=4, show_label=False, placeholder="Enter text and press enter, or upload a file", container=False, lines=3, ) submit_btn = gr.Button("🚀 Send", scale=0) submit_click = submit_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then( bot, [txt, chatbot, oai_key, system_prompt, seed, temp, max_tokens, model], [txt, chatbot], ) submit_click.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False) 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]) txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then( bot, [txt, chatbot, oai_key, system_prompt, seed, temp, max_tokens, model], [txt, chatbot], ) txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False) file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False, postprocess=False) img_msg = img_btn.upload(add_img, [chatbot, img_btn], [chatbot], queue=False, postprocess=False) 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], js=""" (chat_history) => { // Convert the chat history to a JSON string const history_json = JSON.stringify(chat_history); // Create a Blob from the JSON string const blob = new Blob([history_json], {type: 'application/json'}); // Create a download link const url = URL.createObjectURL(blob); const a = document.createElement('a'); a.href = url; a.download = 'chat_history.json'; document.body.appendChild(a); a.click(); document.body.removeChild(a); URL.revokeObjectURL(url); } """) dl_button = gr.Button("File download") dl_button.click(lambda: None, [chatbot], js=""" (chat_history) => { // Attempt to extract content enclosed in backticks with an optional filename const contentRegex = /```(\\S*\\.(\\S+))?\\n?([\\s\\S]*?)```/; const match = contentRegex.exec(chat_history[chat_history.length - 1][1]); if (match && match[3]) { // Extract the content and the file extension const content = match[3]; const fileExtension = match[2] || 'txt'; // Default to .txt if extension is not found const filename = match[1] || `download.${fileExtension}`; // Create a Blob from the content const blob = new Blob([content], {type: `text/${fileExtension}`}); // Create a download link for the Blob const url = URL.createObjectURL(blob); const a = document.createElement('a'); a.href = url; // If the filename from the chat history doesn't have an extension, append the default a.download = filename.includes('.') ? filename : `${filename}.${fileExtension}`; document.body.appendChild(a); a.click(); document.body.removeChild(a); URL.revokeObjectURL(url); } else { // Inform the user if the content is malformed or missing alert('Sorry, the file content could not be found or is in an unrecognized format.'); } } """) import_button.upload(import_history, inputs=[chatbot, import_button], outputs=[chatbot]) demo.queue().launch()