Create app.py

app.py

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+import gradio as gr
+import os
+import openai
+import base64
+
+# Read API key from environment variable
+OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+if not OPENAI_API_KEY:
+    raise ValueError("API key not found. Please set the OPENAI_API_KEY environment variable.")
+
+openai.api_key = OPENAI_API_KEY
+
+global_system_prompt = None
+global_model = 'gpt-4'
+
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+def convert_history_to_openai_format(history):
+    """
+    Convert chat history to OpenAI format.
+    
+    Parameters:
+    history (list of tuples): The chat history where each tuple consists of (message, sender).
+    
+    Returns:
+    list of dict: The formatted history for OpenAI with "role" as either "user" or "assistant".
+    """
+    global global_system_prompt
+    if global_system_prompt is None:
+        global_system_prompt = "You are a helpful assistant."
+        
+    formatted_history = [{"role": "system", "content": global_system_prompt}]
+    for user_msg, assistant_msg in history:
+        if isinstance(user_msg, tuple) and ('.png' in user_msg[0] or '.jpg' in user_msg[0]):
+            encoded_image = encode_image(user_msg[0])
+            text = 'Help me based on the image'
+            if user_msg[1] != '':
+                text = user_msg[1]
+            content = [{'type':'text', 'text':text}, {'type':'image_url', 'image_url':{'url':f'data:image/jpeg;base64,{encoded_image}'}}]
+            formatted_history.append({"role": 'user', "content": content})
+        else:
+            formatted_history.append({"role": 'user', "content": user_msg})
+        if isinstance(assistant_msg, str):
+            formatted_history.append({"role": 'assistant', "content": assistant_msg})
+    
+    return formatted_history
+
+def bot(history):
+    global global_model
+    response = openai.ChatCompletion.create(
+        model=global_model,
+        messages=convert_history_to_openai_format(history)
+    )
+    
+    chatbot_message = response.choices[0].message['content'].strip()
+    history[-1][1] = chatbot_message
+    return history
+
+def add_message(history, message):
+    if len(message["files"]) > 0:
+        for x in message["files"]:
+            history.append(((x, message["text"]), None))
+    else:
+        if message["text"] != '':
+            history.append((message["text"], None))
+    return history, gr.MultimodalTextbox(value=None, interactive=False)
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### Chatbot Playground")
+            chatbot = gr.Chatbot(label="Chatbot:", bubble_full_width=False, show_copy_button=True, min_width=400,
+                                 avatar_images=(os.path.join(os.getcwd(), 'user.png'), os.path.join(os.getcwd(), 'ai.png')))
+            chat_input = gr.MultimodalTextbox(interactive=True, placeholder="Enter message or upload file...", show_label=False)
+
+    # Connect chat_input to bot function to handle input and provide responses
+    chat_input.submit(lambda message, history: bot(add_message(history, message)), [chat_input, chatbot], [chatbot, chat_input])
+
+# Launch the Gradio interface
+demo.launch()