Create app.py

app.py

@@ -0,0 +1,39 @@
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+peft_model_id = f"Bsbell21/llm_instruction_generator"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+def input_from_text(text):
+  return "<s>[INST]Use the provided input to create an instruction that could have been used to generate the response with an LLM.\n" + text + "[/INST]"
+
+def get_instruction(text):
+  inputs = mixtral_tokenizer(input_from_text(text), return_tensors="pt")
+
+  outputs = merged_model.generate(
+      **inputs,
+      max_new_tokens=150,
+      generation_kwargs={"repetition_penalty" : 1.7}
+  )
+  # print(mixtral_tokenizer.decode(outputs[0], skip_special_tokens=True))
+  print(mixtral_tokenizer.decode(outputs[0], skip_special_tokens=True).split("[/INST]")[1])
+
+if __name__ == "__main__":
+    # make a gradio interface
+    import gradio as gr
+
+    gr.Interface(
+        get_instruction,
+        [
+            gr.Textbox(lines=10, label="LLM Response"),
+        ],
+        gr.Textbox(label="LLM Predicted Prompt"),
+        title="LLM-Prompt-Predictor",
+        description="Prompt Predictor Based on LLM Response",
+    ).launch()