Update app.py

app.py

@@ -1,30 +1,39 @@
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import JSONResponse
 import gradio as gr
-from llama_cpp import Llama
 
-llm = Llama(model_path="model.gguf", n_ctx=4000, n_threads=2, chat_format="chatml")
-  
-def generate(message, history,temperature=0.3,max_tokens=512):
-    system_prompt = "You are OpenChat, an userful AI assistant."
-    formatted_prompt = [{"role": "system", "content": system_prompt}]
-    for user_prompt, bot_response  in history:
-        formatted_prompt.append({"role": "user", "content": user_prompt})
-        formatted_prompt.append({"role": "assistant", "content": bot_response })
-    formatted_prompt.append({"role": "user", "content": message})
-    stream_response = llm.create_chat_completion(messages=formatted_prompt, temperature=temperature, max_tokens=max_tokens, stream=True)
-    response  = ""
-    for chunk in stream_response:
-        if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]:
-            response  += chunk['choices'][0]["delta"]["content"]
-        yield response 
+app = FastAPI()
 
-mychatbot = gr.Chatbot(
-avatar_images=["user.png", "botoc.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
-        
-iface = gr.ChatInterface(fn=generate, chatbot=mychatbot, retry_btn=None, undo_btn=None)
+llm = gr.Llama(model_path="model.gguf", n_ctx=4000, n_threads=2, chat_format="chatml")
 
-with gr.Blocks() as demo:
-    gr.HTML("<center><h1>Tomoniai's Chat with OpenChat-3.5</h1></center>")
-    iface.render()
+@app.post("/api/v1/chat")
+async def chat_post(request: Request):
+    data = await request.json()
+    message = data.get("message")
+    history = data.get("history", [])
+    temperature = data.get("temperature", 0.3)
+    max_tokens = data.get("max_tokens", 512)
 
-demo.queue().launch(show_api=False, server_name="0.0.0.0")
-    
+    async def generate():
+        system_prompt = "You are OpenChat, a useful AI assistant."
+        formatted_prompt = [{"role": "system", "content": system_prompt}]
+        for user_prompt, bot_response  in history:
+            formatted_prompt.append({"role": "user", "content": user_prompt})
+            formatted_prompt.append({"role": "assistant", "content": bot_response })
+        formatted_prompt.append({"role": "user", "content": message})
+        stream_response = llm.create_chat_completion(messages=formatted_prompt, temperature=temperature, max_tokens=max_tokens, stream=True)
+        response  = ""
+        for chunk in stream_response:
+            if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]:
+                response  += chunk['choices'][0]["delta"]["content"]
+            yield response
+
+    return JSONResponse(content={"response": await generate()})
+
+@app.get("/api/v1/chat")
+async def chat_get():
+    return {"message": "Send a POST request to this endpoint to chat."}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)