model integration

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import metadata_transformer
 
 def greet(name):
     return "Hello " + name + "!!"
@@ -30,7 +31,7 @@ output = create_output_component()
 
 # Define the Gradio interface.
 interface = gr.Interface(
-    fn=greet,  # This function will handle the logic and transformations.
+    fn=metadata_transformer.translate,  # This function will handle the logic and transformations.
     inputs=inputs,
     outputs="text",
     # live=True,

metadata_transformer.py

@@ -0,0 +1,28 @@
+from transformers import pipeline
+from transformers import AutoTokenizer
+import torch
+
+
+model = "meta-llama/Llama-2-7b-chat-hf"
+
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+def translate(schema_input, schema_target, pipeline):
+    sequences = pipeline(
+        '{} \n Translate the schema metadata file above to the schema: {}'.format(schema_input, schema_target),
+        do_sample=True,
+        top_k=10,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+        max_length=200,
+    )
+    return sequences[0]['generated_text']
+
+