Add examples

README.md

@@ -19,4 +19,3 @@ names, when given the output of Hex-Rays decompiler output of a function.  More
 ## TODO
 
 * We currently use `transformers` which de-quantizes the gguf.  This is easy but inefficient.  Can we use llama.cpp or Ollama with zerogpu?
-* Add examples

app.py

@@ -1,7 +1,7 @@
 import frontmatter
 import gradio as gr
+import json
 import spaces
-import torch
 
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
@@ -29,6 +29,8 @@ example = """int __fastcall sub_B0D04(int a1, int a2)
   return result;
 }"""
 
+examples = [j["input"] for j in json.load(open("gpt4_juiced_dataset.json"))]
+
 # Then create the pipeline with the model and tokenizer
 pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
 
@@ -70,10 +72,12 @@ def predict(code):
     return pipe_out[0]["generated_text"]
 
 
+
 demo = gr.Interface(
     fn=predict,
-    inputs=gr.Text(value=example, label="Hex-Rays decompiler output"),
+    inputs=gr.Text(label="Hex-Rays decompiler output"),
     outputs=gr.JSON(label="Aidapal Output"),
     description=frontmatter.load("README.md").content,
+    examples=examples
 )
 demo.launch()