import gradio as gr import os, time, re, json, base64, asyncio, threading, uuid, io import numpy as np import soundfile as sf from pydub import AudioSegment from openai import OpenAI from websockets import connect from dotenv import load_dotenv # Load secrets load_dotenv() OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") ASSISTANT_ID = os.getenv("ASSISTANT_ID") client = OpenAI(api_key=OPENAI_API_KEY) HEADERS = {"Authorization": f"Bearer {OPENAI_API_KEY}", "OpenAI-Beta": "realtime=v1"} WS_URI = "wss://api.openai.com/v1/realtime?intent=transcription" connections = {} class WebSocketClient: def __init__(self, uri, headers, client_id): self.uri = uri self.headers = headers self.client_id = client_id self.websocket = None self.queue = asyncio.Queue(maxsize=10) self.transcript = "" self.loop = asyncio.new_event_loop() async def connect(self): try: self.websocket = await connect(self.uri, additional_headers=self.headers) with open("openai_transcription_settings.json", "r") as f: await self.websocket.send(f.read()) await asyncio.gather(self.receive_messages(), self.send_audio_chunks()) except Exception as e: print(f"๐Ÿ”ด WebSocket Connection Failed: {e}") def run(self): asyncio.set_event_loop(self.loop) self.loop.run_until_complete(self.connect()) def enqueue_audio_chunk(self, sr, arr): if not self.queue.full(): asyncio.run_coroutine_threadsafe(self.queue.put((sr, arr)), self.loop) async def send_audio_chunks(self): while True: sr, arr = await self.queue.get() if arr.ndim > 1: arr = arr.mean(axis=1) if np.max(np.abs(arr)) > 0: arr = arr / np.max(np.abs(arr)) int16 = (arr * 32767).astype(np.int16) buf = io.BytesIO() sf.write(buf, int16, sr, format='WAV', subtype='PCM_16') audio = AudioSegment.from_file(buf, format="wav").set_frame_rate(24000) out = io.BytesIO() audio.export(out, format="wav") out.seek(0) await self.websocket.send(json.dumps({ "type": "input_audio_buffer.append", "audio": base64.b64encode(out.read()).decode() })) async def receive_messages(self): async for msg in self.websocket: data = json.loads(msg) if data["type"] == "conversation.item.input_audio_transcription.delta": self.transcript += data["delta"] def create_ws(): cid = str(uuid.uuid4()) client = WebSocketClient(WS_URI, HEADERS, cid) threading.Thread(target=client.run, daemon=True).start() connections[cid] = client return cid def send_audio(chunk, cid): if not cid or cid not in connections: return "Connecting..." sr, arr = chunk if len(connections[cid].transcript) > 1000: connections[cid].transcript = "" connections[cid].enqueue_audio_chunk(sr, arr) return connections[cid].transcript.strip() def clear_transcript(cid): if cid in connections: connections[cid].transcript = "" return "" def handle_chat(user_input, history, thread_id, image_url): if not OPENAI_API_KEY or not ASSISTANT_ID: return "โŒ Missing secrets!", history, thread_id, image_url try: if thread_id is None: thread = client.beta.threads.create() thread_id = thread.id client.beta.threads.messages.create(thread_id=thread_id, role="user", content=user_input) run = client.beta.threads.runs.create(thread_id=thread_id, assistant_id=ASSISTANT_ID) while True: status = client.beta.threads.runs.retrieve(thread_id=thread_id, run_id=run.id) if status.status == "completed": break time.sleep(1) msgs = client.beta.threads.messages.list(thread_id=thread_id) for msg in reversed(msgs.data): if msg.role == "assistant": content = msg.content[0].text.value history.append({"role": "user", "content": user_input}) history.append({"role": "assistant", "content": content}) match = re.search( r'https://raw\.githubusercontent\.com/AndrewLORTech/surgical-pathology-manual/main/[\w\-/]*\.png', content ) if match: image_url = match.group(0) break return "", history, thread_id, image_url except Exception as e: return f"โŒ {e}", history, thread_id, image_url def send_transcript_to_assistant(transcript, history, thread_id, image_url, cid): if not transcript.strip(): return gr.update(), history, thread_id, image_url if cid in connections: connections[cid].transcript = "" return handle_chat(transcript, history, thread_id, image_url) def clear_chat_and_transcript(client_id): if client_id in connections: connections[client_id].transcript = "" return [], "", None, None # Fix image viewer fallback def update_image_display(image_url): if image_url and isinstance(image_url, str) and image_url.startswith("http"): return image_url return None # UI with gr.Blocks(theme=gr.themes.Soft()) as app: gr.Markdown("# ๐Ÿ“„ Document AI Assistant") gr.HTML(""" """) chat_state = gr.State([]) thread_state = gr.State() image_state = gr.State() client_id = gr.State() with gr.Row(equal_height=True): with gr.Column(scale=1): image_display = gr.Image(label="๐Ÿ–ผ๏ธ Document", type="filepath", show_download_button=False) with gr.Column(scale=2): chat = gr.Chatbot(label="๐Ÿ’ฌ Chat", height=460, type="messages") with gr.Row(): user_prompt = gr.Textbox(placeholder="Ask your question...", show_label=False, scale=6) send_btn = gr.Button("Send", variant="primary", scale=2) with gr.Accordion("๐ŸŽค Voice Transcription", open=False) as voice_section: gr.Markdown("**๐ŸŽ™๏ธ Tap below to record your voice**") voice_input = gr.Audio(label="", streaming=True, elem_id="record-audio") voice_transcript = gr.Textbox(label="Transcript", lines=2, interactive=False) with gr.Row(): ask_btn = gr.Button("๐ŸŸข Ask", elem_id="ask-btn") clear_chat_btn = gr.Button("๐Ÿงน Clear Chat", elem_id="clear-chat-btn") # Functional bindings send_btn.click(fn=handle_chat, inputs=[user_prompt, chat_state, thread_state, image_state], outputs=[user_prompt, chat, thread_state, image_state]) image_state.change(fn=update_image_display, inputs=image_state, outputs=image_display) voice_input.stream(fn=send_audio, inputs=[voice_input, client_id], outputs=voice_transcript, stream_every=0.5) ask_btn.click(fn=send_transcript_to_assistant, inputs=[voice_transcript, chat_state, thread_state, image_state, client_id], outputs=[user_prompt, chat, thread_state, image_state]) clear_chat_btn.click(fn=clear_chat_and_transcript, inputs=[client_id], outputs=[chat, voice_transcript, thread_state, image_state]) app.load(fn=create_ws, outputs=[client_id]) app.launch()