Delete main.py

main.py

@@ -1,128 +0,0 @@
-import os
-import requests
-from typing import List, Optional, Dict, Any
-
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel, Field
-import gradio as gr
-
-# ===== Config =====
-HF_TOKEN = os.getenv("HF_TOKEN", "")
-ROUTER_URL = "https://router.huggingface.co/v1/chat/completions"
-
-DEFAULT_MODEL = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-1.5B-Instruct")
-
-# ===== FastAPI (OpenAI-ish) =====
-api = FastAPI(title="My Hosted-Model Proxy (API + UI)")
-
-class ChatMessage(BaseModel):
-    role: str
-    content: str
-
-class ChatReq(BaseModel):
-    model: str = Field(default=DEFAULT_MODEL)
-    messages: List[ChatMessage]
-    max_tokens: int = Field(default=256, ge=1, le=2048)
-    temperature: float = Field(default=0.7, ge=0.0, le=2.0)
-    stream: bool = False
-
-@api.get("/health")
-def health():
-    return {"status": "ok", "model_default": DEFAULT_MODEL, "proxying": True}
-
-@api.get("/v1/models")
-def models():
-    return {"object": "list", "data": [{"id": DEFAULT_MODEL, "object": "model", "owned_by": "hf-router"}]}
-
-@api.post("/v1/chat/completions")
-def chat(req: ChatReq):
-    if not HF_TOKEN:
-        raise HTTPException(500, "HF_TOKEN missing. Add it in Space Settings → Secrets as HF_TOKEN.")
-
-    payload = req.model_dump()
-    # Convert Pydantic messages to plain dicts
-    payload["messages"] = [m.model_dump() for m in req.messages]
-
-    r = requests.post(
-        ROUTER_URL,
-        headers={
-            "Authorization": f"Bearer {HF_TOKEN}",
-            "Content-Type": "application/json",
-        },
-        json=payload,
-        timeout=120,
-    )
-
-    # Router errors are still JSON; keep them readable
-    try:
-        out = r.json()
-    except Exception:
-        raise HTTPException(502, f"Router returned non-JSON: {r.text[:500]}")
-
-    if r.status_code >= 400:
-        raise HTTPException(r.status_code, out)
-
-    return out
-
-# ===== Gradio UI =====
-def ui_chat(user_message: str, history: list, model: str, temperature: float, max_tokens: int):
-    """
-    history: list of [user, assistant] pairs from Gradio
-    """
-    messages: List[Dict[str, str]] = []
-    # Optional system prompt to make it feel natural
-    messages.append({"role": "system", "content": "Be natural, concise, and helpful."})
-
-    for u, a in history:
-        messages.append({"role": "user", "content": u})
-        messages.append({"role": "assistant", "content": a})
-
-    messages.append({"role": "user", "content": user_message})
-
-    req = {
-        "model": model,
-        "messages": messages,
-        "temperature": float(temperature),
-        "max_tokens": int(max_tokens),
-        "stream": False,
-    }
-
-    if not HF_TOKEN:
-        return "HF_TOKEN is missing. Add it in Space Settings → Secrets as HF_TOKEN."
-
-    r = requests.post(
-        ROUTER_URL,
-        headers={"Authorization": f"Bearer {HF_TOKEN}", "Content-Type": "application/json"},
-        json=req,
-        timeout=120,
-    )
-    data = r.json()
-    if r.status_code >= 400:
-        return f"Error: {data}"
-
-    return data["choices"][0]["message"]["content"]
-
-with gr.Blocks(title="My AI (Hosted Model) — UI + API") as demo:
-    gr.Markdown("## 💬 Chat UI (Hosted model via HF Router)\nThis Space also exposes an **OpenAI-style API** at `/v1/chat/completions`.")
-
-    with gr.Row():
-        model_dd = gr.Textbox(value=DEFAULT_MODEL, label="Model", interactive=True)
-    with gr.Row():
-        temp = gr.Slider(0, 2, value=0.7, step=0.1, label="Temperature")
-        mx = gr.Slider(32, 1024, value=256, step=16, label="Max tokens")
-
-    gr.ChatInterface(
-        fn=lambda msg, hist: ui_chat(msg, hist, model_dd.value, temp.value, mx.value),
-        title="Chat",
-        description="Type a message. This calls the hosted model and returns the assistant response.",
-    )
-
-    gr.Markdown(
-        "**API endpoints:**\n"
-        "- `GET /health`\n"
-        "- `GET /v1/models`\n"
-        "- `POST /v1/chat/completions`\n"
-    )
-
-# Mount Gradio onto FastAPI at /
-app = gr.mount_gradio_app(api, demo, path="/")