Update app.py

app.py

@@ -1,26 +1,25 @@
-import gradio as gr
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-# Load the pre-trained GPT2 model and tokenizer
+
 model = GPT2LMHeadModel.from_pretrained("gpt2")
 tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
 
-def generate_text(prompt, max_length=50):
-    input_ids = tokenizer.encode(prompt, return_tensors="pt")
-    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
-    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
-    return generated_text
-
-def predict(input_text):
-    output = generate_text(input_text)
-    return output
+def generate_code(prompt, max_length=200):
+    full_prompt = f"Generate Python code for {prompt}:```python\n"
+    input_ids = tokenizer.encode(full_prompt, return_tensors="pt")
+    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, temperature=0.7)
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=False)
+    
+ 
+    start = generated_text.find("```python") + len("```python")
+    end = generated_text.find("```", start)
+    if end == -1:
+        end = len(generated_text)
+    code = generated_text[start:end].strip()
+    
+    return code
 
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(lines=2, placeholder="Enter your text here..."),
-    outputs="text",
-    title="GPT2 Text Generation",
-    description="Generate text using the GPT2 model."
-)
 
-iface.launch()
+prompt = "a function to calculate the factorial of a number"
+malicious_code = generate_code(prompt)
+print(malicious_code)