format

app.py

@@ -12,9 +12,7 @@ print("Downloading model")
 
 tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
 model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    gguf_file=filename,
-    device_map="auto"
+    model_id, gguf_file=filename, device_map="auto"
 )
 
 # Then create the pipeline with the model and tokenizer
@@ -33,6 +31,7 @@ system = """<s>[INST]You are an expert at analyzing code that has been decompile
 Analyze the following IDA Hex Rays pseudocode and generate a valid JSON object containing the keys 'function_name','comment', and an array 'variables' explaining what the code does, suggest a function name based on the analysis of the code, and new variable names based on the analysis of the code.[/INST]</s>
 """
 
+
 @spaces.GPU
 def predict(code):
     prompt = f"""{system}
@@ -45,6 +44,11 @@ def predict(code):
     print(f"Tokenized: {tokenizer.tokenize(prompt)}")
     return pipe(prompt)
 
-demo = gr.Interface(fn=predict, inputs="text", outputs="text",
-                    description=frontmatter.load("README.md").content)
-demo.launch()
+
+demo = gr.Interface(
+    fn=predict,
+    inputs="text",
+    outputs="text",
+    description=frontmatter.load("README.md").content,
+)
+demo.launch()