Create app.py

app.py

@@ -0,0 +1,132 @@
+from openai import AsyncAssistantEventHandler
+from openai import AsyncOpenAI
+import gradio as gr
+import asyncio
+
+# Set your OpenAI API key here
+client = AsyncOpenAI(
+    api_key="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"
+)
+
+assistantID = "asst_pMk1lyBSaVZPulq44RvIJUNe"
+
+class EventHandler(AsyncAssistantEventHandler):
+    def __init__(self) -> None:
+        super().__init__()
+        self.response_text = ""
+
+    async def on_text_created(self, text) -> None:
+        self.response_text += str(text)
+
+    async def on_text_delta(self, delta, snapshot):
+        self.response_text += str(delta.value)
+
+    async def on_text_done(self, text):
+        pass
+
+    async def on_tool_call_created(self, tool_call):
+        self.response_text += f"\n[Tool Call]: {str(tool_call.type)}\n"
+
+    async def on_tool_call_delta(self, delta, snapshot):
+        if snapshot.id != getattr(self, "current_tool_call", None):
+            self.current_tool_call = snapshot.id
+            self.response_text += f"\n[Tool Call Delta]: {str(delta.type)}\n"
+
+        if delta.type == 'code_interpreter':
+            if delta.code_interpreter.input:
+                self.response_text += str(delta.code_interpreter.input)
+            if delta.code_interpreter.outputs:
+                self.response_text += "\n\n[Output]:\n"
+                for output in delta.code_interpreter.outputs:
+                    if output.type == "logs":
+                        self.response_text += f"\n{str(output.logs)}"
+
+    async def on_tool_call_done(self, text):
+        pass
+
+# Initialize session variables
+session_data = {"assistant_id": assistantID, "thread_id": None}
+
+async def initialize_thread():
+    # Create a Thread
+    thread = await client.beta.threads.create()
+    # Store thread ID in session_data for later use
+    session_data["thread_id"] = thread.id
+
+async def generate_response(user_input):
+    assistant_id = session_data["assistant_id"]
+    thread_id = session_data["thread_id"]
+
+    # Add a Message to the Thread
+    oai_message = await client.beta.threads.messages.create(
+        thread_id=thread_id,
+        role="user",
+        content=user_input
+    )
+
+    # Create and Stream a Run
+    event_handler = EventHandler()
+
+    async with client.beta.threads.runs.stream(
+        thread_id=thread_id,
+        assistant_id=assistant_id,
+        instructions="""You are a Code Interpreter to analyze JSON files with RTL comments. Here is the format of the files :  
+[
+    {
+        "context_id": "",
+        "date_created": "",
+        "text": " ",
+        "user_id": "",
+        "referer": "",
+        "status": ",
+        "thumbs": [
+            {
+                "user_id": "",
+                "score": "up",
+                "date": ""
+            },
+            {
+                "user_id": "",
+                "score": "down",
+                "date": ""
+            },
+    }
+]
+You will search dates ("date_created" of a comment and "date"  of the related thumbs),  calculate the total number of up scores and down scores. You will answer questions about "context_id", "text" and "referers".""",
+        event_handler=event_handler,
+    ) as stream:
+        # Yield incremental updates
+        async for _ in stream:
+            await asyncio.sleep(0.1)  # Small delay to mimic streaming
+            yield event_handler.response_text
+
+# Gradio interface function (generator)
+async def gradio_chat_interface(user_input):
+    # Create a new event loop if none exists (or if we are in a new thread)
+    try:
+        loop = asyncio.get_running_loop()
+    except RuntimeError:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+
+    # Initialize the thread if not already done
+    if session_data["thread_id"] is None:
+        await initialize_thread()
+
+    # Generate and yield responses
+    async for response in generate_response(user_input):
+        yield response
+
+# Set up Gradio interface with streaming
+interface = gr.Interface(
+    fn=gradio_chat_interface,
+    inputs="text",
+    outputs="markdown",
+    title="OpenAI Interpreter with Gradio",
+    description="Ask anything and get an AI-generated response in real-time.",
+    live=False,  # Important to allow streaming-like behavior
+    allow_flagging="never"
+)
+
+# Launch the Gradio app
+interface.launch()