कंप्यूटर का इस्तेमाल

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3.8-flash",
    contents="Search for 'Gemini API' on Google.",
    config=types.GenerateContentConfig(
        tools=[types.Tool(
            computer_use=types.ComputerUse(
                environment=types.Environment.ENVIRONMENT_BROWSER,
            )
        )]
    )
)

print(response.text)

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI();

const response = await ai.models.generateContent({
  model: 'gemini-3.8-flash',
  contents: "Search for 'Gemini API' on Google.",
  config: {
    tools: [{
      computerUse: {
        environment: "ENVIRONMENT_BROWSER",
      }
    }]
  }
});

console.log(response.text);

from google import genai
from google.genai.types import (
    Content,
    Part,
    GenerateContentConfig,
    Tool,
    ComputerUse,
    Environment,
    ThinkingConfig,
)

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3.8-flash",
    contents=[
        Content(
            role="user",
            parts=[
                Part(text="Find a flight from SF to Hawaii on Jun 30th, coming back on Jul 6th"),
            ],
        )
    ],
    config=GenerateContentConfig(
        tools=[
            Tool(
                computer_use=ComputerUse(
                    environment=Environment.ENVIRONMENT_BROWSER,
                    enable_prompt_injection_detection=True,
                ),
            ),
        ],
        thinking_config=ThinkingConfig(
            include_thoughts=True
        ),
    )
)

print(response.text)

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI();

const response = await ai.models.generateContent({
  model: 'gemini-3.8-flash',
  contents: [
    {
      role: 'user',
      parts: [{ text: "Find a flight from SF to Hawaii on Jun 30th, coming back on Jul 6th" }]
    }
  ],
  config: {
    tools: [{
      computerUse: {
        environment: "ENVIRONMENT_BROWSER",
        enable_prompt_injection_detection: true
      }
    }],
    thinkingConfig: {
      includeThoughts: true
    }
  }
});

console.log(response.text);

curl -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.8-flash:generateContent?key=$GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": {
          "text": "Find me a flight from SF to Hawaii on Jun 30th, coming back on Jul 6th. Start by navigating directly to flights.google.com"
        }
      }
    ],
    "tools": [
      {
        "computer_use": {
          "environment": "ENVIRONMENT_BROWSER",
          "enable_prompt_injection_detection": true
        }
      }
    ]
  }'

from typing import Any, List, Tuple
import time

def denormalize_x(x: int, screen_width: int) -> int:
    """Convert normalized x coordinate (0-1000) to actual pixel coordinate."""
    return int(x / 1000 * screen_width)

def denormalize_y(y: int, screen_height: int) -> int:
    """Convert normalized y coordinate (0-1000) to actual pixel coordinate."""
    return int(y / 1000 * screen_height)

def execute_function_calls(interaction, page, screen_width, screen_height):
    results = []
    function_calls = []

    # Parse function calls from candidate response
    parts = candidate.content.parts if hasattr(candidate, 'content') else []
    if not parts and hasattr(candidate, 'function_calls'):
        function_calls = candidate.function_calls
    else:
        for part in parts:
            if part.function_call:
                function_calls.append(part.function_call)

    for function_call in function_calls:
        action_result = {}
        fname = function_call.name
        args = function_call.args
        print(f"  -> Executing: {fname} (Intent: {args.get('intent', 'N/A')})")

        try:
            if fname == "open_app":
                pass # Handled / already open
            elif fname in ("click", "double_click", "triple_click", "middle_click", "right_click", "move", "long_press"):
                actual_x = denormalize_x(args["x"], screen_width)
                actual_y = denormalize_y(args["y"], screen_height)

                if fname == "click":
                    page.mouse.click(actual_x, actual_y)
                elif fname == "double_click":
                    page.mouse.dblclick(actual_x, actual_y)
                elif fname == "right_click":
                    page.mouse.click(actual_x, actual_y, button="right")
                elif fname == "middle_click":
                    page.mouse.click(actual_x, actual_y, button="middle")
                elif fname == "move":
                    page.mouse.move(actual_x, actual_y)
            elif fname == "type":
                actual_x = denormalize_x(args["x"], screen_width) if "x" in args else None
                actual_y = denormalize_y(args["y"], screen_height) if "y" in args else None
                text = args["text"]
                press_enter = args.get("press_enter", False)

                if actual_x is not None and actual_y is not None:
                    page.mouse.click(actual_x, actual_y)
                # Clear field first
                page.keyboard.press("Meta+A")
                page.keyboard.press("Backspace")
                page.keyboard.type(text)
                if press_enter:
                    page.keyboard.press("Enter")
            elif fname == "navigate":
                page.goto(args["url"])
            elif fname == "go_back":
                page.go_back()
            elif fname == "go_forward":
                page.go_forward()
            elif fname == "wait":
                time.sleep(args.get("seconds", 1))
            else:
                print(f"Warning: Custom or unhandled function {fname}")

            page.wait_for_load_state(timeout=5000)
            time.sleep(1)

        except Exception as e:
            print(f"Error executing {fname}: {e}")
            action_result = {"error": str(e)}

        results.append((fname, function_call.id, action_result))

    return results

function denormalizeX(x, screenWidth) {
    // Convert normalized x coordinate (0-1000) to actual pixel coordinate.
    return Math.floor((x / 1000) * screenWidth);
}

function denormalizeY(y, screenHeight) {
    // Convert normalized y coordinate (0-1000) to actual pixel coordinate.
    return Math.floor((y / 1000) * screenHeight);
}

async function executeFunctionCalls(candidate, page, screenWidth, screenHeight) {
    const results = [];
    let functionCalls = [];

    // Parse function calls from candidate response
    const parts = candidate.content?.parts || [];
    if (parts.length === 0 && candidate.functionCalls) {
        functionCalls = candidate.functionCalls;
    } else {
        for (const part of parts) {
            if (part.functionCall) {
                functionCalls.push(part.functionCall);
            }
        }
    }

    for (const functionCall of functionCalls) {
        const actionResult = {};
        const fname = functionCall.name;
        const args = functionCall.args;
        console.log(`  -> Executing: ${fname} (Intent: ${args.intent || 'N/A'})`);

        try {
            if (fname === "open_app") {
                // Handled / already open
            } else if (["click", "double_click", "triple_click", "middle_click", "right_click", "move", "long_press"].includes(fname)) {
                const actualX = denormalizeX(args.x, screenWidth);
                const actualY = denormalizeY(args.y, screenHeight);

                if (fname === "click") {
                    await page.mouse.click(actualX, actualY);
                } else if (fname === "double_click") {
                    await page.mouse.dblclick(actualX, actualY);
                } else if (fname === "right_click") {
                    await page.mouse.click(actualX, actualY, { button: "right" });
                } else if (fname === "middle_click") {
                    await page.mouse.click(actualX, actualY, { button: "middle" });
                } else if (fname === "move") {
                    await page.mouse.move(actualX, actualY);
                }
            } else if (fname === "type") {
                const actualX = args.x !== undefined ? denormalizeX(args.x, screenWidth) : null;
                const actualY = args.y !== undefined ? denormalizeY(args.y, screenHeight) : null;
                const text = args.text;
                const pressEnter = args.press_enter || false;

                if (actualX !== null && actualY !== null) {
                    await page.mouse.click(actualX, actualY);
                }
                // Clear field first
                await page.keyboard.press("Meta+A");
                await page.keyboard.press("Backspace");
                await page.keyboard.type(text);
                if (pressEnter) {
                    await page.keyboard.press("Enter");
                }
            } else if (fname === "navigate") {
                await page.goto(args.url);
            } else if (fname === "go_back") {
                await page.goBack();
            } else if (fname === "go_forward") {
                await page.goForward();
            } else if (fname === "wait") {
                await new Promise(resolve => setTimeout(resolve, (args.seconds || 1) * 1000));
            } else {
                console.log(`Warning: Custom or unhandled function ${fname}`);
            }

            await page.waitForLoadState('load', { timeout: 5000 }).catch(() => {});
            await new Promise(resolve => setTimeout(resolve, 1000));
        } catch (e) {
            console.log(`Error executing ${fname}: ${e}`);
            actionResult.error = e.message;
        }

        results.push([fname, functionCall.id, actionResult]);
    }

    return results;
}

def get_function_responses(page, results):
    screenshot_bytes = page.screenshot(type="png")
    current_url = page.url
    function_responses = []
    for name, call_id, result in results:
        function_responses.append({
            "type": "function_result",
            "name": name,
            "call_id": call_id,
            "result": [
                {
                    "type": "text",
                    "text": json.dumps({"url": current_url, **result})
                },
                {
                    "type": "image",
                    "data": base64.b64encode(screenshot_bytes).decode("utf-8"),
                    "mime_type": "image/png"
                }
            ]
        })
    return function_responses

async function getFunctionResponses(page, results) {
    const screenshotBuffer = await page.screenshot({ type: 'png' });
    const screenshotBase64 = screenshotBuffer.toString('base64');
    const currentUrl = page.url();
    const functionResponses = [];

    for (const [name, callId, result] of results) {
        functionResponses.push({
            type: "function_result",
            name: name,
            call_id: callId,
            result: [
                {
                    type: "text",
                    text: JSON.stringify({ url: currentUrl, ...result })
                },
                {
                    type: "image",
                    data: screenshotBase64,
                    mime_type: "image/png"
                }
            ]
        });
    }
    return functionResponses;
}

import time
from typing import Any, List, Tuple
from playwright.sync_api import sync_playwright
from google import genai
from google.genai import types

client = genai.Client()

SCREEN_WIDTH = 1440
SCREEN_HEIGHT = 900

print("Initializing browser...")
playwright = sync_playwright().start()
browser = playwright.chromium.launch(headless=False)
context = browser.new_context(viewport={"width": SCREEN_WIDTH, "height": SCREEN_HEIGHT})
page = context.new_page()

# Paste helper functions execute_function_calls and get_function_responses here

try:
    page.goto("https://ai.google.dev/gemini-api/docs")

    config = types.GenerateContentConfig(
        tools=[types.Tool(computer_use=types.ComputerUse(
            environment=types.Environment.ENVIRONMENT_BROWSER,
            enable_prompt_injection_detection=True
        ))],
        thinking_config=types.ThinkingConfig(include_thoughts=True),
    )

    initial_screenshot = page.screenshot(type="png")
    USER_PROMPT = "Go to ai.google.dev/gemini-api/docs and search for pricing."
    print(f"Goal: {USER_PROMPT}")

    contents = [
        types.Content(role="user", parts=[
            types.Part(text=USER_PROMPT),
            types.Part.from_bytes(data=initial_screenshot, mime_type='image/png')
        ])
    ]

    # Agent Loop
    turn_limit = 5
    for i in range(turn_limit):
        print(f"\n--- Turn {i+1} ---")
        print("Thinking...")
        response = client.models.generate_content(
            model='gemini-3.8-flash',
            contents=contents,
            config=config,
        )

        candidate = response.candidates[0]
        contents.append(candidate.content)

        has_function_calls = any(part.function_call for part in candidate.content.parts)
        if not has_function_calls:
            text_response = " ".join(
                part.text for part in candidate.content.parts if hasattr(part, 'text')
            )
            print("Agent finished:", text_response)
            break

        print("Executing actions...")
        results = execute_function_calls(candidate, page, SCREEN_WIDTH, SCREEN_HEIGHT)

        print("Capturing state...")
        function_responses = get_function_responses(page, results)

        contents.append(
            types.Content(role="user", parts=[types.Part(function_response=fr) for fr in function_responses])
        )

finally:
    print("Closing browser...")
    browser.close()
    playwright.stop()

import { chromium } from 'playwright';
import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI();

// Constants for screen dimensions
const SCREEN_WIDTH = 1440;
const SCREEN_HEIGHT = 900;

console.log("Initializing browser...");
const browser = await chromium.launch({ headless: false });
const context = await browser.newContext({
    viewport: { width: SCREEN_WIDTH, height: SCREEN_HEIGHT }
});
const page = await context.newPage();

// Define helper functions. Copy/paste from steps 3 and 4:
// function denormalizeX(...)
// function denormalizeY(...)
// async function executeFunctionCalls(...)
// async function getFunctionResponses(...)

try {
    await page.goto("https://ai.google.dev/gemini-api/docs");

    const config = {
        tools: [{
            computerUse: {
                environment: "ENVIRONMENT_BROWSER",
                enable_prompt_injection_detection: true
            }
        }],
        thinkingConfig: { includeThoughts: true }
    };

    const initialScreenshotBuffer = await page.screenshot({ type: 'png' });
    const initialScreenshotBase64 = initialScreenshotBuffer.toString('base64');
    const USER_PROMPT = "Go to ai.google.dev/gemini-api/docs and search for pricing.";
    console.log(`Goal: ${USER_PROMPT}`);

    const contents = [
        {
            role: "user",
            parts: [
                { text: USER_PROMPT },
                {
                    inlineData: {
                        data: initialScreenshotBase64,
                        mimeType: "image/png"
                    }
                }
            ]
        }
    ];

    // Agent Loop
    const turnLimit = 5;
    for (let i = 0; i < turnLimit; i++) {
        console.log(`\n--- Turn ${i + 1} ---`);
        console.log("Thinking...");
        const response = await ai.models.generateContent({
            model: 'gemini-3.8-flash',
            contents: contents,
            config: config
        });

        const candidate = response.candidates[0];
        contents.push(candidate.content);

        const hasFunctionCalls = candidate.content.parts.some(part => part.functionCall);
        if (!hasFunctionCalls) {
            const textResponse = candidate.content.parts
                .filter(part => part.text)
                .map(part => part.text)
                .join(" ");
            console.log("Agent finished:", textResponse);
            break;
        }

        console.log("Executing actions...");
        const results = await executeFunctionCalls(candidate, page, SCREEN_WIDTH, SCREEN_HEIGHT);

        console.log("Capturing state...");
        const functionResponses = await getFunctionResponses(page, results);

        contents.push({
            role: "user",
            parts: functionResponses.map(fr => ({
                ...fr
            }))
        });
    }
} finally {
    console.log("Closing browser...");
    await browser.close();
}

from google import genai
from google.genai import types

client = genai.Client()

yield_to_user_tool = types.FunctionDeclaration(
    name="yield_to_user",
    description="Yields control back to the user for assistance or verification when an automated action is unsafe or ambiguous.",
    parameters=types.Schema(
        type="OBJECT",
        properties={
            "reason": types.Schema(
                type="STRING",
                description="The reason why the agent is yielding control to the human."
            )
        },
        required=["reason"]
    )
)

response = client.models.generate_content(
    model="gemini-3.8-flash",
    contents="Click the submit button. If you need a second factor authentication code, ask me.",
    config=types.GenerateContentConfig(
        tools=[
            types.Tool(
                computer_use=types.ComputerUse(
                    environment="ENVIRONMENT_MOBILE",
                    excluded_predefined_functions=["click"]
                )
            ),
            yield_to_user_tool
        ]
    )
)

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3.8-flash",
    contents="Clean up the local folder by archiving old logs.",
    config=types.GenerateContentConfig(
        tools=[
            types.Tool(
                computer_use=types.ComputerUse(
                    environment=types.Environment.ENVIRONMENT_DESKTOP,
                    disabled_safety_policies=[
                        types.SafetyPolicy.DATA_MODIFICATION
                    ]
                )
            )
        ]
    )
)

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI();

const response = await ai.models.generateContent({
  model: 'gemini-3.8-flash',
  contents: "Clean up the local folder by archiving old logs.",
  config: {
    tools: [{
      computerUse: {
        environment: "ENVIRONMENT_DESKTOP",
        disabledSafetyPolicies: [
          "DATA_MODIFICATION"
        ]
      }
    }]
  }
});

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Search for flight deals and summarize top results.",
    config=types.GenerateContentConfig(
        tools=[
            types.Tool(
                computer_use=types.ComputerUse(
                    environment="ENVIRONMENT_DESKTOP",
                    enable_prompt_injection_detection=True,
                )
            )
        ]
    ),
)

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI();

const response = await ai.models.generateContent({
    model: "gemini-3.5-flash",
    contents: "Search for flight deals and summarize top results.",
    config: {
        tools: [{
            computerUse: {
                environment: "ENVIRONMENT_DESKTOP",
                enablePromptInjectionDetection: true,
            }
        }]
    }
});

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:generateContent?key=${GEMINI_API_KEY}" \
-H 'Content-Type: application/json' \
-d '{
  "contents": [{
    "parts": [{"text": "Search for flight deals and summarize top results."}]
  }],
  "tools": [{
    "computer_use": {
      "environment": "ENVIRONMENT_DESKTOP",
      "enable_prompt_injection_detection": true
    }
  }]
}'

def get_safety_confirmation(safety_decision):
    # Prompt user for confirmation
    print(f"Safety confirmation required: {safety_decision.get('explanation', '')}")
    return "CONTINUE" # Or TERMINATE

# Inside execute_function_calls, check for safety_decision:
if 'safety_decision' in function_call.args:
    decision = get_safety_confirmation(function_call.args['safety_decision'])
    if decision == "TERMINATE":
        break
    # Include safety_acknowledgement inside the action result
    action_result["safety_acknowledgement"] = True