Update app.py

app.py

@@ -0,0 +1,73 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List, Literal, Optional
+from huggingface_hub import InferenceClient
+from fastapi.responses import JSONResponse
+import uuid
+import time
+import uvicorn
+
+app = FastAPI()
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# OpenAI-compatible request message
+class Message(BaseModel):
+    role: Literal["system", "user", "assistant"]
+    content: str
+
+# OpenAI-compatible request body
+class ChatCompletionRequest(BaseModel):
+    model: str = "zephyr-7b-beta"
+    messages: List[Message]
+    temperature: Optional[float] = 0.7
+    top_p: Optional[float] = 0.95
+    max_tokens: Optional[int] = 512
+    stream: Optional[bool] = False
+
+# OpenAI-compatible response message
+class Choice(BaseModel):
+    index: int
+    message: Message
+    finish_reason: Optional[str] = "stop"
+
+# OpenAI-compatible full response
+class ChatCompletionResponse(BaseModel):
+    id: str
+    object: str = "chat.completion"
+    created: int
+    model: str
+    choices: List[Choice]
+
+@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
+async def chat_completions(request: ChatCompletionRequest):
+    # Build HuggingFace-style message list
+    messages = [{"role": m.role, "content": m.content} for m in request.messages]
+
+    # Generate chat completion
+    response_text = ""
+    for chunk in client.chat_completion(
+        messages,
+        max_tokens=request.max_tokens,
+        temperature=request.temperature,
+        top_p=request.top_p,
+        stream=False,
+    ):
+        response_text += chunk.choices[0].delta.content
+
+    # Build OpenAI-style response
+    chat_response = ChatCompletionResponse(
+        id=f"chatcmpl-{uuid.uuid4().hex}",
+        created=int(time.time()),
+        model=request.model,
+        choices=[
+            Choice(
+                index=0,
+                message=Message(role="assistant", content=response_text),
+            )
+        ]
+    )
+    return JSONResponse(content=chat_response.dict())
+
+# Run this file directly
+if __name__ == "__main__":
+    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)