app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from playwright.sync_api import sync_playwright
from flax import linen as nn
from jax import random
import jax
import jax.numpy as jnp

# Define LLaVA model parameters
MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
MAX_LENGTH = 512
NUM_BEAMS = 5

# Define Flax model for action generation
class ActionModel(nn.Module):
    vocab_size: int
    hidden_size: int
    num_layers: int

    def setup(self):
        self.embedding = nn.Embed(self.vocab_size, self.hidden_size)
        self.lstm = nn.LSTM(self.hidden_size, self.hidden_size, num_layers=self.num_layers)
        self.dense = nn.Dense(self.vocab_size)

    def __call__(self, inputs, init_state):
        embedded = self.embedding(inputs)
        output, new_state = self.lstm(embedded, init_state)
        logits = self.dense(output)
        return logits, new_state

# Initialize Flax model and get its initial state
vocab_size = 50257  # Adjust this if needed for Zephyr-7b-beta
hidden_size = 1024
num_layers = 2
key = random.PRNGKey(0)
model = ActionModel(vocab_size, hidden_size, num_layers)
init_state = model.lstm.initialize_carry(key, (1, hidden_size))

# Function to generate actions using Zephyr-7b-beta model
def generate_actions(input_text, browser, page):
    # Load Zephyr-7b-beta model
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)

    # Prepare input for Zephyr-7b-beta
    inputs = tokenizer(input_text, return_tensors="pt")
    inputs = inputs.to(model.device)

    # Generate response (use pipeline for Zephyr-7b-beta)
    generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
    outputs = generator(input_text, max_length=MAX_LENGTH, num_beams=NUM_BEAMS, temperature=0.7)

    # Decode response and extract actions
    response = outputs[0]['generated_text']
    actions = response.split("\n")
    
    # Perform actions
    for action in actions:
        if "open website" in action:
            website = action.split(" ")[-1]
            page.goto(website)
        elif "click" in action:
            selector = action.split(" ")[-1]
            page.click(selector)
        elif "type" in action:
            text = action.split(" ")[-1]
            page.type(text)
        elif "submit" in action:
            page.press("Enter")
        else:
            print(f"Action not recognized: {action}")

# Function to initialize browser and page
def initialize_browser():
    with sync_playwright() as p:
        browser = p.chromium.launch()
        page = browser.new_page()
        return browser, page

# Gradio interface
def run_agent(input_text):
    with sync_playwright() as p:
        browser, page = initialize_browser()
        generate_actions(input_text, browser, page)
        return f"Successfully executed actions based on: {input_text}"

iface = gr.Interface(
    fn=run_agent,
    inputs=gr.Textbox(label="Enter your request"),
    outputs=gr.Textbox(label="Response"),
    title="Automated Agent",
    description="Enter a task or instruction for the agent to perform."
)
iface.launch()