app.py

import json
import os
import shutil
import time
import uuid
import tempfile
import atexit
import unicodedata
from io import BytesIO
from threading import Timer
from typing import Any, Dict, List, Optional
from datetime import datetime

import gradio as gr
import torch
import spaces
from dotenv import load_dotenv
from PIL import Image, ImageDraw

# Selenium Imports
from selenium import webdriver
from selenium.webdriver.chrome.service import Service as ChromeService
from selenium.webdriver.chrome.options import Options as ChromeOptions
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from webdriver_manager.chrome import ChromeDriverManager

# Smolagents imports
from smolagents import CodeAgent, tool, AgentImage
from smolagents.memory import ActionStep, TaskStep
from smolagents.models import ChatMessage, Model, MessageRole
from smolagents.gradio_ui import GradioUI, stream_to_gradio
from smolagents.monitoring import LogLevel

# Transformers for Fara Model
from transformers import (
    Qwen2_5_VLForConditionalGeneration,
    AutoProcessor,
)
from qwen_vl_utils import process_vision_info

load_dotenv(override=True)

# -----------------------------------------------------------------------------
# CONFIGURATION & CONSTANTS
# -----------------------------------------------------------------------------

HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_API_KEY")
if HF_TOKEN:
    from huggingface_hub import login
    login(token=HF_TOKEN)

# Browser Sandbox Config
WIDTH = 1024
HEIGHT = 768
TMP_DIR = "./tmp/"
if not os.path.exists(TMP_DIR):
    os.makedirs(TMP_DIR)

# -----------------------------------------------------------------------------
# MODEL INITIALIZATION (Fara-7B / Qwen2.5-VL)
# -----------------------------------------------------------------------------

print("Loading Fara Model... This may take a moment.")
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MODEL_ID_F = "microsoft/Fara-7B" 

# Global model variables
model_f = None
processor_f = None

try:
    processor_f = AutoProcessor.from_pretrained(MODEL_ID_F, trust_remote_code=True)
    model_f = Qwen2_5_VLForConditionalGeneration.from_pretrained(
        MODEL_ID_F,
        trust_remote_code=True,
        torch_dtype=torch.bfloat16 if DEVICE == "cuda" else torch.float32,
        device_map="auto",
    )
    print(f"Fara Model loaded successfully on {DEVICE}")
except Exception as e:
    print(f"Error loading Fara Model: {e}")
    print("Falling back to Qwen/Qwen2.5-VL-7B-Instruct...")
    try:
        MODEL_ID_F = "Qwen/Qwen2.5-VL-7B-Instruct" 
        processor_f = AutoProcessor.from_pretrained(MODEL_ID_F, trust_remote_code=True)
        model_f = Qwen2_5_VLForConditionalGeneration.from_pretrained(
            MODEL_ID_F,
            trust_remote_code=True,
            torch_dtype=torch.bfloat16 if DEVICE == "cuda" else torch.float32,
            device_map="auto",
        )
        print(f"Fallback Model ({MODEL_ID_F}) loaded successfully.")
    except Exception as inner_e:
        print(f"Critical error loading model: {inner_e}")


# -----------------------------------------------------------------------------
# GPU ISOLATED INFERENCE FUNCTION
# -----------------------------------------------------------------------------

@spaces.GPU(duration=120)
def run_model_inference(formatted_messages, max_tokens=1024, stop_sequences=None):
    """
    Runs inference on the GPU worker.
    """
    global model_f, processor_f
    
    if model_f is None:
        raise ValueError("Model is not loaded.")

    text = processor_f.apply_chat_template(
        formatted_messages, tokenize=False, add_generation_prompt=True
    )
    
    image_inputs, video_inputs = process_vision_info(formatted_messages)
    
    inputs = processor_f(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    
    inputs = inputs.to(model_f.device)

    with torch.no_grad():
        generated_ids = model_f.generate(
            **inputs,
            max_new_tokens=max_tokens,
            stop_strings=stop_sequences,
            tokenizer=processor_f.tokenizer, 
        )

    generated_ids_trimmed = [
        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor_f.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )[0]

    return output_text


class FaraLocalModel(Model):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def __call__(
        self,
        messages: List[Dict[str, Any]],
        stop_sequences: Optional[List[str]] = None,
        **kwargs,
    ) -> ChatMessage:
        
        formatted_messages = []
        for msg in messages:
            role = msg["role"]
            content = msg["content"]
            new_content = []
            
            if isinstance(content, str):
                new_content.append({"type": "text", "text": content})
            elif isinstance(content, list):
                for item in content:
                    if isinstance(item, str):
                        new_content.append({"type": "text", "text": item})
                    elif isinstance(item, dict):
                        if "type" in item:
                            if item["type"] == "image":
                                val = item.get("image") or item.get("url") or item.get("path")
                                new_content.append({"type": "image", "image": val})
                            else:
                                new_content.append(item)
            
            formatted_messages.append({"role": role, "content": new_content})

        output_text = run_model_inference(
            formatted_messages=formatted_messages,
            max_tokens=kwargs.get("max_tokens", 1024),
            stop_sequences=stop_sequences
        )

        return ChatMessage(
            role=MessageRole.ASSISTANT,
            content=output_text,
        )

# -----------------------------------------------------------------------------
# SELENIUM CHROME SANDBOX
# -----------------------------------------------------------------------------

def get_system_chrome_path():
    # Common paths for chromium in Linux/HF Spaces
    paths = [
        "/usr/bin/chromium",
        "/usr/bin/chromium-browser",
        "/usr/bin/google-chrome",
    ]
    for p in paths:
        if os.path.exists(p):
            return p
    return None

class SeleniumSandbox:
    def __init__(self, width=1024, height=768):
        self.width = width
        self.height = height
        self.tmp_dir = tempfile.mkdtemp(prefix="chrome_sandbox_")
        
        # Setup Chrome Options
        chrome_opts = ChromeOptions()
        
        # Use system binary if available (fixes status 127 in HF Spaces)
        binary_path = get_system_chrome_path()
        if binary_path:
            print(f"Using system Chrome binary at: {binary_path}")
            chrome_opts.binary_location = binary_path
        
        chrome_opts.add_argument("--headless=new") 
        chrome_opts.add_argument(f"--user-data-dir={self.tmp_dir}")
        chrome_opts.add_argument(f"--window-size={width},{height}")
        chrome_opts.add_argument("--no-sandbox") # Crucial for containers
        chrome_opts.add_argument("--disable-dev-shm-usage") # Crucial for containers
        chrome_opts.add_argument("--disable-gpu")
        chrome_opts.add_argument("--disable-extensions")
        
        # Initialize Driver
        try:
            # Check for system driver first
            system_driver_path = "/usr/bin/chromedriver"
            if os.path.exists(system_driver_path):
                print(f"Using system ChromeDriver at: {system_driver_path}")
                service = ChromeService(executable_path=system_driver_path)
            else:
                print("Using webdriver_manager to install ChromeDriver...")
                service = ChromeService(ChromeDriverManager().install())
            
            self.driver = webdriver.Chrome(service=service, options=chrome_opts)
            self.driver.set_window_size(width, height)
            self.driver.get("about:blank")
            print(f"Selenium Chrome Driver started successfully.")
            
        except Exception as e:
            print(f"Failed to initialize Selenium: {e}")
            self.cleanup()
            raise e

    def get_screenshot(self):
        """Returns screenshot as PIL Image"""
        png_data = self.driver.get_screenshot_as_png()
        return Image.open(BytesIO(png_data))

    def move_mouse_and_click(self, x, y, click_type="left"):
        try:
            body = self.driver.find_element(By.TAG_NAME, "body")
            actions = ActionChains(self.driver)
            actions.move_to_element_with_offset(body, 0, 0)
            actions.move_by_offset(x, y)
            if click_type == "left":
                actions.click()
            elif click_type == "right":
                actions.context_click()
            elif click_type == "double":
                actions.double_click()
            actions.perform()
        except Exception as e:
            print(f"Error in move_mouse_and_click: {e}")

    def drag_and_drop(self, x1, y1, x2, y2):
        try:
            body = self.driver.find_element(By.TAG_NAME, "body")
            actions = ActionChains(self.driver)
            actions.move_to_element_with_offset(body, 0, 0)
            actions.move_by_offset(x1, y1)
            actions.click_and_hold()
            actions.move_by_offset(x2 - x1, y2 - y1)
            actions.release()
            actions.perform()
        except Exception as e:
            print(f"Error in drag_and_drop: {e}")

    def type_text(self, text):
        actions = ActionChains(self.driver)
        actions.send_keys(text)
        actions.perform()
        
    def press_key(self, key_name):
        try:
            k = getattr(Keys, key_name.upper(), None)
            if not k:
                if key_name.lower() == "enter": k = Keys.ENTER
                elif key_name.lower() == "space": k = Keys.SPACE
                elif key_name.lower() == "backspace": k = Keys.BACK_SPACE
                elif key_name.lower() == "esc": k = Keys.ESCAPE
                else: k = key_name 
            actions = ActionChains(self.driver)
            actions.send_keys(k)
            actions.perform()
        except Exception as e:
            print(f"Error pressing key: {e}")

    def scroll(self, amount, direction="down"):
        try:
            scroll_y = amount * 100 
            if direction == "up":
                scroll_y = -scroll_y
            self.driver.execute_script(f"window.scrollBy(0, {scroll_y});")
        except Exception as e:
            print(f"Error scrolling: {e}")

    def cleanup(self):
        try:
            if hasattr(self, 'driver'):
                self.driver.quit()
        except:
            pass
        shutil.rmtree(self.tmp_dir, ignore_errors=True)

# -----------------------------------------------------------------------------
# AGENT SETUP
# -----------------------------------------------------------------------------

SYSTEM_PROMPT_TEMPLATE = """You are a browser automation assistant controlling a Google Chrome web browser. The current date is <<current_date>>.

<action process>
You will be given a task to solve in several steps. At each step you will perform an action.
After each action, you'll receive an updated screenshot of the browser. 
Then you will proceed as follows, with these sections: don't skip any!

Short term goal: ...
What I see: ...
Reflection: ...
Action:
```python
click(254, 308)
```<end_code>

Always format your action ('Action:' part) as Python code blocks as shown above.
</action_process>

<tools>
On top of performing computations in the Python code snippets that you create, you only have access to these tools to interact with the browser:
{%- for tool in tools.values() %}
- {{ tool.name }}: {{ tool.description }}
    Takes inputs: {{tool.inputs}}
    Returns an output of type: {{tool.output_type}}
{%- endfor %}
</tools>

<click_guidelines>
The browser has a resolution of <<resolution_x>>x<<resolution_y>> pixels.
NEVER USE HYPOTHETIC OR ASSUMED COORDINATES, USE TRUE COORDINATES that you can see from the screenshot.
Use precise coordinates based on the current screenshot.
Whenever you click, MAKE SURE to click in the middle of the button, text, link or any other clickable element.
In the screenshot you will see a green crosshair displayed over the position of your last click.
</click_guidelines>

<general_guidelines>
Execute one action at a time.
Use `open_url` to navigate to websites.
Use `click` to navigate links and interface elements.
Use `type_text` to input into forms.
Use `scroll` to see more content.
If you get stuck, try using `open_url` to search on Google.
</general_guidelines>
""".replace("<<current_date>>", datetime.now().strftime("%A, %d-%B-%Y"))

def draw_marker_on_image(image_copy, click_coordinates):
    x, y = click_coordinates
    draw = ImageDraw.Draw(image_copy)
    cross_size, linewidth = 10, 3
    # Draw cross
    draw.line((x - cross_size, y, x + cross_size, y), fill="green", width=linewidth)
    draw.line((x, y - cross_size, x, y + cross_size), fill="green", width=linewidth)
    draw.ellipse(
        (x - cross_size * 2, y - cross_size * 2, x + cross_size * 2, y + cross_size * 2),
        outline="green",
        width=linewidth,
    )
    return image_copy

class SeleniumVisionAgent(CodeAgent):
    """Agent for Browser automation with Selenium and Vision"""

    def __init__(
        self,
        model: Model,
        data_dir: str,
        sandbox: SeleniumSandbox,
        max_steps: int = 20,
        verbosity_level: LogLevel = 2,
        **kwargs,
    ):
        self.sandbox = sandbox
        self.data_dir = data_dir
        
        # Initialize
        print(f"Browser size: {self.sandbox.width}x{self.sandbox.height}")
        os.makedirs(self.data_dir, exist_ok=True)

        super().__init__(
            tools=[],
            model=model,
            max_steps=max_steps,
            verbosity_level=verbosity_level,
            **kwargs,
        )
        
        self.prompt_templates["system_prompt"] = SYSTEM_PROMPT_TEMPLATE.replace(
            "<<resolution_x>>", str(self.sandbox.width)
        ).replace("<<resolution_y>>", str(self.sandbox.height))

        self.register_tools()
        self.step_callbacks.append(self.take_screenshot_callback)

    def register_tools(self):

        @tool
        def click(x: int, y: int) -> str:
            """
            Performs a left-click at the specified coordinates.
            Args:
                x: The x coordinate (horizontal position).
                y: The y coordinate (vertical position).
            """
            self.sandbox.move_mouse_and_click(x, y, "left")
            self.click_coordinates = [x, y]
            return f"Clicked at ({x}, {y})"

        @tool
        def right_click(x: int, y: int) -> str:
            """
            Performs a right-click at the specified coordinates.
            Args:
                x: The x coordinate.
                y: The y coordinate.
            """
            self.sandbox.move_mouse_and_click(x, y, "right")
            self.click_coordinates = [x, y]
            return f"Right-clicked at ({x}, {y})"

        @tool
        def double_click(x: int, y: int) -> str:
            """
            Performs a double-click at the specified coordinates.
            Args:
                x: The x coordinate.
                y: The y coordinate.
            """
            self.sandbox.move_mouse_and_click(x, y, "double")
            self.click_coordinates = [x, y]
            return f"Double-clicked at ({x}, {y})"

        @tool
        def type_text(text: str) -> str:
            """
            Types the specified text.
            Args:
                text: The text to type.
            """
            clean_text = unicodedata.normalize("NFD", text)
            self.sandbox.type_text(clean_text)
            return f"Typed text: '{clean_text}'"

        @tool
        def press_key(key: str) -> str:
            """
            Presses a keyboard key (e.g., 'enter', 'backspace', 'esc').
            Args:
                key: The key name.
            """
            self.sandbox.press_key(key)
            return f"Pressed key: {key}"

        @tool
        def drag_and_drop(x1: int, y1: int, x2: int, y2: int) -> str:
            """
            Drags from (x1, y1) and drops at (x2, y2).
            Args:
                x1: Start x coordinate.
                y1: Start y coordinate.
                x2: End x coordinate.
                y2: End y coordinate.
            """
            self.sandbox.drag_and_drop(x1, y1, x2, y2)
            return f"Dragged from [{x1}, {y1}] to [{x2}, {y2}]"

        @tool
        def scroll(amount: int, direction: str = "down") -> str:
            """
            Scrolls the page.
            Args:
                amount: The amount to scroll (1-10).
                direction: "up" or "down".
            """
            self.sandbox.scroll(amount, direction)
            return f"Scrolled {direction} by {amount}"

        @tool
        def wait(seconds: float) -> str:
            """
            Waits for the specified number of seconds.
            Args:
                seconds: The duration to wait.
            """
            time.sleep(seconds)
            return f"Waited for {seconds} seconds"

        @tool
        def open_url(url: str) -> str:
            """
            Navigates the browser to the specified URL.
            Args:
                url: The URL to open.
            """
            if not url.startswith(("http://", "https://")):
                url = "https://" + url
            try:
                self.sandbox.driver.get(url)
                time.sleep(2)
                title = self.sandbox.driver.title
                return f"Opened URL: {url}. Page Title: {title}"
            except Exception as e:
                return f"Failed to open URL: {e}"

        @tool
        def go_back() -> str:
            """
            Goes back to the previous page in history.
            """
            self.sandbox.driver.back()
            return "Went back one page"

        self.tools["click"] = click
        self.tools["right_click"] = right_click
        self.tools["double_click"] = double_click
        self.tools["type_text"] = type_text
        self.tools["press_key"] = press_key
        self.tools["drag_and_drop"] = drag_and_drop
        self.tools["scroll"] = scroll
        self.tools["wait"] = wait
        self.tools["open_url"] = open_url
        self.tools["go_back"] = go_back


    def take_screenshot_callback(self, memory_step: ActionStep, agent=None) -> None:
        """Takes a screenshot and saves it to memory"""
        current_step = memory_step.step_number
        time.sleep(1.0) # Wait for renders

        image = self.sandbox.get_screenshot()

        # Save to disk
        screenshot_path = os.path.join(self.data_dir, f"step_{current_step:03d}.png")
        image.save(screenshot_path)

        image_copy = image.copy()
        if getattr(self, "click_coordinates", None):
            image_copy = draw_marker_on_image(image_copy, self.click_coordinates)

        self.last_marked_screenshot = AgentImage(screenshot_path)
        
        # Cleanup old images in memory to save RAM
        for previous_memory_step in agent.memory.steps:
            if isinstance(previous_memory_step, ActionStep) and previous_memory_step.step_number <= current_step - 1:
                previous_memory_step.observations_images = None
            elif isinstance(previous_memory_step, TaskStep):
                previous_memory_step.task_images = None

        memory_step.observations_images = [image_copy]
        self.click_coordinates = None


def create_agent(data_dir, sandbox):
    model = FaraLocalModel()
    return SeleniumVisionAgent(
        model=model,
        data_dir=data_dir,
        sandbox=sandbox,
        max_steps=30,
        verbosity_level=2
    )

def generate_interaction_id(session_uuid):
    return f"{session_uuid}_{int(time.time())}"

def get_agent_summary_erase_images(agent):
    for memory_step in agent.memory.steps:
        if hasattr(memory_step, "observations_images"):
            memory_step.observations_images = None
        if hasattr(memory_step, "task_images"):
            memory_step.task_images = None
    return agent.write_memory_to_messages()

def save_final_status(folder, status: str, summary, error_message=None) -> None:
    try:
        with open(os.path.join(folder, "metadata.json"), "w") as output_file:
            output_file.write(
                json.dumps(
                    {"status": status, "summary": summary, "error_message": error_message},
                    default=str
                )
            )
    except Exception as e:
        print(f"Failed to save metadata: {e}")

# -----------------------------------------------------------------------------
# UI & APP
# -----------------------------------------------------------------------------

custom_css = """
.modal-container { margin: var(--size-16) auto!important; }
.browser-container { position: relative; width: 100%; height: 600px; border: 1px solid #444; background: #222; display: flex; align-items: center; justify-content: center; overflow: hidden; }
.browser-image { max-width: 100%; max-height: 100%; object-fit: contain; }
#chatbot { height: 800px!important; }
"""

class EnrichedGradioUI(GradioUI):
    def interact_with_agent(
        self,
        task_input,
        stored_messages,
        session_state,
        session_uuid,
        consent_storage,
        request: gr.Request,
    ):
        interaction_id = generate_interaction_id(session_uuid)
        data_dir = os.path.join(TMP_DIR, interaction_id)
        
        sandbox = SeleniumSandbox(width=WIDTH, height=HEIGHT)
        agent = create_agent(data_dir=data_dir, sandbox=sandbox)
        session_state["agent"] = agent

        try:
            stored_messages.append(gr.ChatMessage(role="user", content=task_input))
            yield stored_messages, None

            screenshot = sandbox.get_screenshot()
            
            for msg in stream_to_gradio(
                agent,
                task=task_input,
                task_images=[screenshot],
                reset_agent_memory=False,
            ):
                if hasattr(agent, "last_marked_screenshot") and msg.content == "-----":
                    stored_messages.append(
                        gr.ChatMessage(
                            role="assistant",
                            content={
                                "path": agent.last_marked_screenshot.to_string(),
                                "mime_type": "image/png",
                            },
                        )
                    )
                    yield stored_messages, agent.last_marked_screenshot.to_string()
                else:
                    stored_messages.append(msg)
                    yield stored_messages, None

            if consent_storage:
                summary = get_agent_summary_erase_images(agent)
                save_final_status(data_dir, "completed", summary=summary)
            
            yield stored_messages, None

        except Exception as e:
            error_message = f"Error in interaction: {str(e)}"
            print(error_message)
            stored_messages.append(
                gr.ChatMessage(role="assistant", content="Run failed:\n" + error_message)
            )
            yield stored_messages, None
        finally:
            sandbox.cleanup()

theme = gr.themes.Default(
    font=["Oxanium", "sans-serif"], primary_hue="amber", secondary_hue="blue"
)

with gr.Blocks(theme=theme, css=custom_css) as demo:
    session_uuid_state = gr.State(lambda: str(uuid.uuid4()))
    session_state = gr.State({})
    stored_messages = gr.State([])
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Fara CUA - Chrome Agent 🌐")
            
            task_input = gr.Textbox(
                value="Go to google.com and search for 'Hugging Face'",
                label="Task",
                lines=3
            )
            run_btn = gr.Button("Start Task", variant="primary")
            stop_btn = gr.Button("Stop", variant="secondary")
            consent_storage = gr.Checkbox(label="Save logs locally?", value=True)
            
            gr.Examples(
                examples=[
                    "Go to google.com and search for 'Hugging Face', then click the first link.",
                    "Go to wikipedia.org, type 'Python' in search, and click the search button.",
                ],
                inputs=task_input
            )

        with gr.Column(scale=3):
            with gr.Row():
                with gr.Column(scale=1):
                    chatbot_display = gr.Chatbot(
                        label="Agent Trace",
                        type="messages",
                        height=800,
                        avatar_images=(None, "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/mascot_smol.png"),
                    )
                with gr.Column(scale=1):
                    gr.Markdown("### Latest Browser View")
                    live_browser_view = gr.Image(
                        label="Browser View", 
                        type="filepath", 
                        interactive=False,
                        height=600
                    )

    agent_ui = EnrichedGradioUI(CodeAgent(tools=[], model=Model(), name="init"))

    def interrupt_agent(session_state):
        if "agent" in session_state and hasattr(session_state["agent"], "interrupt_switch"):
            session_state["agent"].interrupt_switch = True
            return "Interrupted"
    
    run_event = run_btn.click(
        fn=agent_ui.interact_with_agent,
        inputs=[
            task_input,
            stored_messages,
            session_state,
            session_uuid_state,
            consent_storage,
        ],
        outputs=[chatbot_display, live_browser_view]
    )
    
    stop_btn.click(fn=interrupt_agent, inputs=[session_state], outputs=[])

if __name__ == "__main__":
    demo.launch(share=True)