new

app.py

@@ -1,48 +1,7 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import gradio as gr
+from fastapi import FastAPI
 
-# Initialize model and tokenizer
-MODEL_ID = "abdelac/tinyllama"
+app = FastAPI()
 
-@cache_resource
-def load_model():
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_ID,
-        torch_dtype=torch.float16,
-        device_map="auto"
-    )
-    return tokenizer, model
-
-tokenizer, model = load_model()
-
-def respond(message, history):
-    # Format chat history
-    prompt = ""
-    for user_msg, assistant_msg in history:
-        prompt += f"Human: {user_msg}\nAssistant: {assistant_msg}\n"
-    prompt += f"Human: {message}\nAssistant:"
-    
-    # Tokenize
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    
-    # Generate
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=256,
-        temperature=0.7,
-        do_sample=True,
-        pad_token_id=tokenizer.eos_token_id
-    )
-    
-    # Decode
-    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
-    return response
-
-# Create chat interface
-gr.ChatInterface(
-    respond,
-    title="TinyLlama Chat",
-    description="Chat with TinyLlama model",
-).launch()
+@app.get("/")
+def greet_json():
+    return {"Hello": "World!"}