Create app.py

app.py

@@ -0,0 +1,55 @@
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+peft_model_id = f"Bsbell21/MarketMail-Bloomz"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    return_dict=True,
+    device_map="auto",
+    load_in_8bit=False
+)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+def make_inference(product, description):
+  batch = tokenizer(f"### INSTRUCTION\nBelow is a product and description, please write a marketing email for this product.\n\n### Product:\n{product}\n### Description:\n{description}\n\n### Marketing Email:\n", return_tensors='pt')
+
+  with torch.cuda.amp.autocast():
+    output_tokens = model.generate(**batch, max_new_tokens=200)
+  return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+'''
+def make_inference(product_name, product_description):
+    batch = tokenizer(
+        f"### Product and Description:\n{product_name}: {product_description}\n\n### Ad:",
+        return_tensors="pt",
+    )
+    
+    batch = {key: value.to('cuda:0') for key, value in batch.items()}
+
+    with torch.cuda.amp.autocast():
+        output_tokens = model.generate(**batch, max_new_tokens=50)
+
+    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+
+'''
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+if __name__ == "__main__":
+    # make a gradio interface
+    import gradio as gr
+
+    gr.Interface(
+        make_inference,
+        [
+            gr.Textbox(lines=2, label="Product Name"),
+            gr.Textbox(lines=5, label="Product Description"),
+        ],
+        gr.Textbox(label="Email Ad"),
+        title="MarketMail-AI",
+        description="MarketMail-AI is a generative model that generates email ads for products.",
+    ).launch()