Create app.py

app.py

@@ -0,0 +1,116 @@
+import gradio as gr
+import openai, os, time
+
+from openai import OpenAI
+from utils import show_json
+
+_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+
+_assistant, _thread = None, None
+
+def create_assistant(client):
+    assistant = client.beta.assistants.create(
+        name="Math Tutor",
+        instructions="You are a personal math tutor. Answer questions briefly, in a sentence or less.",
+        model="gpt-4-1106-preview",
+        tools=[{"type": "code_interpreter"}],
+    )
+    show_json("assistant", assistant)
+    return assistant
+    
+def create_thread(client):
+    thread = client.beta.threads.create()
+    show_json("thread", thread)
+    return thread
+
+def create_message(client, thread, msg):
+    message = client.beta.threads.messages.create(
+        role="user",
+        thread_id=thread.id,
+        content=msg,
+    )
+    show_json("message", message)
+    return message
+
+def create_run(client, assistant, thread):
+    run = client.beta.threads.runs.create(
+        assistant_id=assistant.id,
+        thread_id=thread.id,
+    )
+    show_json("run", run)
+    return run
+
+def wait_on_run(client, thread, run):
+    while run.status == "queued" or run.status == "in_progress":
+        run = client.beta.threads.runs.retrieve(
+            thread_id=thread.id,
+            run_id=run.id,
+        )
+        time.sleep(0.25)
+    show_json("run", run)
+    return run
+
+def list_run_steps(client, thread, run):
+    run_steps = client.beta.threads.runs.steps.list(
+        thread_id=thread.id,
+        run_id=run.id,
+        order="asc",
+    )
+    for step in run_steps.data:
+        step_details = step.step_details
+        #print(json.dumps(show_json("step_details", step_details), indent=4))
+        show_json("step_details", step_details)
+    return run_steps
+    
+def list_messages(client, thread):
+    messages = client.beta.threads.messages.list(
+        thread_id=thread.id
+    )
+    show_json("messages", messages)
+    return messages
+    
+def extract_content_values(data):
+    content_values = []
+    for item in data.data:
+        for content in item.content:
+            if content.type == "text":
+                content_values.append(content.text.value)
+    return content_values
+
+def chat(message, history, openai_api_key):
+    global _client, _assistant, _thread     
+       
+    if _assistant == None:
+        _assistant = create_assistant(_client)
+
+    if _thread == None:
+        _thread = create_thread(_client)
+        
+    create_message(_client, _thread, message)
+
+    # async
+    run = create_run(_client, _assistant, _thread)
+    run = wait_on_run(_client, _thread, run)
+
+    list_run_steps(_client, _thread, run)
+    
+    messages = list_messages(_client, _thread)
+
+    return extract_content_values(messages)[0]
+        
+gr.ChatInterface(
+    chat,
+    chatbot=gr.Chatbot(height=300),
+    textbox=gr.Textbox(placeholder="Type here", container=False, scale=7),
+    title="OpenAI Assistants API",
+    description="Ask AAA Assistant, BBB Assistant, and CCC Assistant any question",
+    retry_btn=None,
+    undo_btn=None,
+    clear_btn="Clear",
+    examples=[["I need to solve the equation '2x + 11 = 7.5'. Can you help me?", "sk-<BringYourOwn>"],
+              ["Generate the first 20 fibbonaci numbers with code.", "sk-<BringYourOwn>"]],
+    cache_examples=False,
+    additional_inputs=[
+        gr.Textbox("sk-", label="OpenAI API Key", type = "password"),
+    ],
+).launch()