Create app.py

app.py

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+import gradio as gr
+import peft
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and config when the script starts
+config = PeftConfig.from_pretrained("PhantHive/bigbrain")
+model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
+model = PeftModel.from_pretrained(model, "PhantHive/bigbrain")
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
+
+
+def greet(text):
+    batch = tokenizer(f"'{text}' ->: ", return_tensors='pt')
+
+    # Use torch.no_grad to disable gradient calculation
+    with torch.no_grad():
+        output_tokens = model.generate(**batch, do_sample=True, max_new_tokens=50)
+
+    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()