Update app.py

app.py

@@ -1,52 +1,31 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-# Initialize the client with the updated model
-client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-# Define the system prompt
-SYSTEM_PROMPT = (
-    "You are Mohamed Abu Basith, an expert programming assistant specializing in software development, debugging, and code optimization. "
-    "When addressing programming-related queries:\n"
-    "- Provide clear, concise solutions with well-structured code examples in the appropriate language\n"
-    "- Include detailed, step-by-step explanations of your reasoning and implementation\n"
-    "- Prioritize efficient, production-ready code while explaining tradeoffs when relevant\n\n"
-    "If asked about your identity, name, or 'what about you', respond exactly with: 'I am Mohamed Abu Basith.' "
-    "For all other non-technical interactions, respond with a friendly, professional greeting "
-    "avoiding technical jargon or programming-related content."
-)
-
-def respond(message, history: list[tuple[str, str]]):
-    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
-    
-    for user_msg, assistant_msg in history:
-        if user_msg:
-            messages.append({"role": "user", "content": user_msg})
-        if assistant_msg:
-            messages.append({"role": "assistant", "content": assistant_msg})
-    
-    messages.append({"role": "user", "content": message})
-    
-    response = ""
-    
-    # Static parameters for generation
-    max_tokens = 512
-    temperature = 0.7
-    top_p = 0.95
-
-    for token in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        content = token.choices[0].delta.content
-        response += content
-        yield response
-
-# Create the Gradio ChatInterface without additional inputs
-demo = gr.ChatInterface(respond)
-
-if __name__ == "__main__":
-    demo.launch()
+import os
+import tempfile
+
+from fastapi import FastAPI
+from fastapi.responses import FileResponse
+
+app = FastAPI()
+
+MODEL_NAME = "Qwen/Qwen3-TTS-12Hz-0.6B-Base"
+
+# Load the model here.
+# Replace this section with the official Qwen code.
+
+@app.get("/")
+def health():
+    return {"status": "running"}
+
+@app.get("/tts")
+def generate(text: str):
+    output_file = tempfile.NamedTemporaryFile(
+        suffix=".wav",
+        delete=False
+    ).name
+
+    # Generate audio here.
+
+    return FileResponse(
+        output_file,
+        media_type="audio/wav",
+        filename="speech.wav",
+    )