Create app.py

Resume Summary Generator initial deployment.

app.py

@@ -0,0 +1,38 @@
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+peft_model_id = "gkrishnan/Resume_Parsing_Model"
+config = PeftConfig.from_pretrained(peft_model_id)
+base_model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    return_dict=True,
+    load_in_8bit=False,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+
+
+def make_inference(resume):
+  batch = tokenizer(f"Write a summary based off this resume.\n\n### Resume:\n{resume}", 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)
+    
+if __name__ == "__main__":
+    import gradio as gr
+
+    gr.Interface(
+    make_inference,
+    [
+        gr.inputs.Textbox(lines=2, label="Resume"),
+    ],
+    gr.outputs.Textbox(label="Summarized Resume"),
+    title="Resume Summary Generator",
+    description="This generates a summary from a Resume",
+).launch()