Create app2.py

app2.py

@@ -0,0 +1,314 @@
+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, files):
+    for file in files:
+        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, files):
+    for file in files:
+        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-turbo-preview", allow_custom_value=True, elem_id="model",
+                            choices=["gpt-4-turbo-preview", "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", file_count="multiple")
+        img_btn = gr.UploadButton("🖼️ Upload", size="sm", file_count="multiple", 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()