Update app.py

app.py

@@ -1,10 +1,6 @@
 import gradio as gr
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-import requests
-import pandas as pd
-import numpy as np
-from datasets import load_dataset
 
 # Load the model and tokenizer from Hugging Face Hub
 model_path = "Canstralian/pentest_ai"  # Replace with your model path if needed
@@ -23,26 +19,14 @@ def generate_text(instruction):
     output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return output_text
 
-# Function to load a sample dataset (this can be replaced with any dataset)
-def load_sample_data():
-    # Load a sample dataset from Hugging Face Datasets
-    dataset = load_dataset("imdb", split="train[:5]")
-    df = pd.DataFrame(dataset)
-    return df.head()  # Show a preview of the first 5 entries
-
 # Gradio interface to interact with the text generation function
 iface = gr.Interface(
     fn=generate_text, 
     inputs=gr.Textbox(lines=2, placeholder="Enter your question or prompt here..."), 
     outputs="text", 
-    live=True,
     title="Pentest AI Text Generator",
     description="Generate text using a fine-tuned model for pentesting-related queries."
 )
 
-# Gradio interface for viewing the sample dataset (optional)
-data_viewer = gr.Interface(fn=load_sample_data, inputs=[], outputs="dataframe", title="Sample Dataset Viewer")
-
-# Launch the interfaces
+# Launch the interface
 iface.launch()
-data_viewer.launch()