Add inference and req

app.py

@@ -2,13 +2,38 @@ import gradio as gr
 import spaces
 import torch
 
+from peft import PeftConfig, PeftModel
+from transformers import LlamaForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+config = PeftConfig.from_pretrained("GGmorello/FLAMES")
+model = LlamaForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    quantization_config=BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+    ),
+)
+model = PeftModel.from_pretrained(model, "GGmorello/FLAMES")
+
+
+MAX_SEQ_LEN = 4096
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+model.config.pad_token = tokenizer.pad_token = tokenizer.unk_token
+
+
+
 zero = torch.Tensor([0]).cuda()
 print(zero.device) # <-- 'cpu' 🤔
 
 @spaces.GPU
-def greet(n):
-    print(zero.device) # <-- 'cuda:0' 🤗
-    return f"Hello {zero + n} Tensor"
+def predict(text):
+    input_ids = tokenizer(text, return_tensors='pt')["input_ids"]
+    generated_ids = model.generate(input_ids, max_new_tokens=256)
+    filling = tokenizer.batch_decode(generated_ids[:, input_ids.shape[1]:], skip_special_tokens = True)[0]
+    return filling
+
 
-demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
+demo = gr.Interface(fn=predict, inputs=gr.Text(), outputs=gr.Text())
 demo.launch()