Update app.py

app.py

@@ -1,3 +1,37 @@
-import gradio as gr
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-gr.load("models/Artples/L-MChat-Small").launch()
+class UserRequest(BaseModel):
+    prompt: str
+
+app = FastAPI()
+
+# Load the model and tokenizer
+model_name = "Artples/L-MChat-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Make sure the model is on CPU
+device = torch.device("cpu")
+model.to(device)
+
+@app.post("/generate/")
+async def generate(request: UserRequest):
+    try:
+        # Tokenize the prompt
+        inputs = tokenizer.encode(request.prompt, return_tensors="pt")
+        inputs = inputs.to(device)
+        
+        # Generate a response from the model
+        output = model.generate(inputs, max_length=100, num_return_sequences=1)
+        response_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        
+        return {"response": response_text}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)