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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from playwright.sync_api import sync_playwright
import json
# Define model and inference parameters
MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
MAX_LENGTH = 512
NUM_BEAMS = 5
TEMPERATURE = 0.7
# Function to generate actions using Zephyr-7b-beta model
def generate_actions(input_text):
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
# Use pipeline for text generation
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
outputs = generator(input_text, max_length=MAX_LENGTH, num_beams=NUM_BEAMS, temperature=TEMPERATURE)
response = outputs[0]['generated_text']
actions = response.split("\n")
return actions
# 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()
actions = generate_actions(input_text)
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}")
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() |