new added OpenAI API kinda

app.py

@@ -1,7 +1,69 @@
-import gradio as gr
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import uvicorn
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load model (Mistral, Mixtral, Llama2, etc. that works on zeroGPU)
+model_id = "mistralai/Mistral-7B-Instruct-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Create app
+app = FastAPI()
+
+# Data format matching OpenAI API
+class Message(BaseModel):
+    role: str
+    content: str
+
+class ChatRequest(BaseModel):
+    model: str
+    messages: list[Message]
+    temperature: float = 0.7
+    top_p: float = 1.0
+    max_tokens: int = 256
+    stream: bool = False
+
+@app.post("/v1/chat/completions")
+async def chat_completions(request: ChatRequest):
+    # Combine chat messages into a prompt
+    prompt = ""
+    for msg in request.messages:
+        prompt += f"{msg.role}: {msg.content}\n"
+    prompt += "assistant:"
+
+    # Generate output
+    output = generator(prompt, max_new_tokens=request.max_tokens, temperature=request.temperature)[0]["generated_text"]
+
+    # Extract assistant response
+    assistant_reply = output.split("assistant:")[-1].strip()
+
+    # Build OpenAI-compatible response
+    return JSONResponse({
+        "id": "chatcmpl-fake001",
+        "object": "chat.completion",
+        "created": 1234567890,
+        "model": request.model,
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": assistant_reply
+                },
+                "finish_reason": "stop"
+            }
+        ],
+        "usage": {
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "total_tokens": 0
+        }
+    })
+
+# Run app if local (Spaces will handle this themselves via gradio)
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -0,0 +1,5 @@
+fastapi
+uvicorn
+transformers
+torch
+huggingface_hub