Update app.py

app.py

@@ -1,35 +1,45 @@
-import torch
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from IPython.display import display, Markdown
+# import torch
+# from peft import PeftModel, PeftConfig
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+# from IPython.display import display, Markdown
 
-peft_model_id = f"adamtappis/marketing_emails_model"
-config = PeftConfig.from_pretrained(peft_model_id)
-model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False)
-tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+# peft_model_id = f"adamtappis/marketing_emails_model"
+# config = PeftConfig.from_pretrained(peft_model_id)
+# model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, 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)
+# 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')
+# 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)
+# 
+#   display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))
 
-  with torch.cuda.amp.autocast():
-    output_tokens = model.generate(**batch, max_new_tokens=200)
+import gradio as gr
+from transformers import pipeline
+pipe = pipeline("Marketing", model="adamtappis/marketing_emails_model")
+demo = gr.Interface.from_pipeline(pipe)
+demo.launch()
 
-  display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))
+# def predict(text):
+#   return pipe(text)[0]["translation_text"]
 
-if __name__ == "__main__":
-    # make a gradio interface
-    import gradio as gr
 
-    gr.Interface(
-        make_inference,
-        [
-            gr.inputs.Textbox(lines=1, label="Product Name"),
-            gr.inputs.Textbox(lines=1, label="Product Description"),
-        ],
-        gr.outputs.Textbox(label="Email"),
-        title="🗣️Marketing Email Generator📄",
-        description="🗣️Marketing Email Generator📄 is a tool that allows you to generate marketing emails for different products",
-    ).launch()
+# if __name__ == "__main__":
+#     # make a gradio interface
+#     import gradio as gr
+# 
+#     gr.Interface(
+#         make_inference,
+#         [
+#             gr.inputs.Textbox(lines=1, label="Product Name"),
+#             gr.inputs.Textbox(lines=1, label="Product Description"),
+#         ],
+#         gr.outputs.Textbox(label="Email"),
+#         title="🗣️Marketing Email Generator📄",
+#         description="🗣️Marketing Email Generator📄 is a tool that allows you to generate marketing emails for different products",
+#     ).launch()