app.py

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
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Function to handle user inputs and generate responses
def generate_text(instruction):
    # Encode the input text to token IDs
    inputs = tokenizer.encode(instruction, return_tensors='pt', truncation=True, max_length=512)
    
    # Generate the output text
    outputs = model.generate(inputs, max_length=150, num_beams=5, temperature=0.7, top_p=0.95, do_sample=True)
    
    # Decode the output and return the response
    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
iface.launch()
data_viewer.launch()