Update app.py

app.py

@@ -1,77 +1,59 @@
-import os
-import httpx
-import numpy as np
-from queue import Queue, Empty
-
-from fastrtc import Stream, StreamHandler, get_stt_model, get_tts_model
-from openai import OpenAI
-
-# Initialize OpenAI client and on-device models
-sambanova_client = OpenAI(
-    api_key=os.getenv("key"),
-    base_url="https://api.deepinfra.com/v1"
-)
-stt_model = get_stt_model()
-tts_model = get_tts_model()
-
-class EchoHandler(StreamHandler):
-    def __init__(self):
-        super().__init__()  # uses default sample rates/layouts
-        self.queue: Queue[tuple[int, np.ndarray]] = Queue()
-
-    def start_up(self) -> None:
-        # Optional: warm up models or state here
-        pass
-
-    def receive(self, frame: tuple[int, np.ndarray]) -> None:
-        # frame is (sample_rate, numpy array)
-        sample_rate, audio_array = frame
-
-        # 1) Transcribe speech → text
-        text = stt_model.stt(frame)
-
-        # 2) Chat completion
-        response = sambanova_client.chat.completions.create(
-            model="mistralai/Mistral-Small-24B-Instruct-2501",
-            messages=[{"role": "user", "content": text}],
-            max_tokens=200,
-        )
-        reply = response.choices[0].message.content
-
-        # 3) Generate TTS chunks and enqueue them
-        for tts_chunk in tts_model.stream_tts_sync(reply):
-            # each tts_chunk is a numpy array of shape (1, N)
-            self.queue.put((sample_rate, tts_chunk))
-
-    def emit(self):
-        try:
-            return self.queue.get_nowait()
-        except Empty:
-            return None  # no audio to send right now
-
-    def copy(self) -> "EchoHandler":
-        return EchoHandler()
-
-    def shutdown(self) -> None:
-        # Optional cleanup
+import gradio as gr
+from fastapi import FastAPI, Request
+import uvicorn
+
+# Initialize FastAPI and Gradio
+app = FastAPI()
+gr_interface = None
+STORAGE_PATH = "storage.txt"
+
+# Initialize storage
+def init_storage():
+    try:
+        with open(STORAGE_PATH, "x") as f:
+            f.write("")
+    except FileExistsError:
         pass
 
-def get_cloudflare_turn_credentials(
-    turn_key_id=None,
-    turn_key_api_token=None,
-    hf_token=None,
-    ttl=600,
-    client: httpx.AsyncClient | None = None,
-):
-    # Replace with your real TURN creds logic
-    return {"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]}
-
-# Wire up the stream with the new handler
-stream = Stream(
-    handler=EchoHandler(),
-    modality="audio",
-    mode="send-receive",
-    rtc_configuration=get_cloudflare_turn_credentials
-)
-
-stream.fastphone()
+init_storage()
+
+# Function to read stored data
+def read_data():
+    with open(STORAGE_PATH, "r") as f:
+        return f.read()
+
+# Function to write data
+def write_data(text):
+    with open(STORAGE_PATH, "w") as f:
+        f.write(text)
+
+# Gradio UI function
+def show_latest_data():
+    return read_data()
+
+# Set up the Gradio Blocks interface
+with gr.Blocks() as gr_app:
+    gr.Markdown("## Latest Sent Text")
+    output = gr.Textbox(label="Stored Text", value=read_data(), interactive=False)
+    refresh_btn = gr.Button("Refresh")
+
+    refresh_btn.click(fn=show_latest_data, inputs=[], outputs=output)
+
+# Mount Gradio to FastAPI
+@app.get("/store")
+async def store_text(request: Request):
+    text = request.query_params.get("text")
+    if text:
+        write_data(text)
+        return {"status": "success", "stored": text}
+    else:
+        return {"status": "error", "message": "No text parameter provided."}
+
+@app.get("/")
+async def root():
+    return gr_app
+
+# Launch Gradio when run as __main__
+if __name__ == "__main__":
+    gr_interface = gr.mount_gradio_app(app, gr_app, path="/")
+    uvicorn.run(app, host="0.0.0.0", port=7860)