Update agent.py

agent.py

@@ -1,6 +1,6 @@
 import os
 from dotenv import load_dotenv
-from typing import TypedDict, Annotated
+from typing import List, Dict, Any, Optional
 from langgraph.graph import START, StateGraph, MessagesState
 from langgraph.graph.message import add_messages
 from langchain_core.messages import AnyMessage, HumanMessage, AIMessage, SystemMessage
@@ -70,7 +70,444 @@ tavily_search_tool = TavilySearch(
     topic="general",
 )
 
-tools = [multiply, add, subtract, divide, wikidata_search, tavily_search_tool]
+@tool
+def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
+    """
+    Save content to a file and return the path.
+    Args:
+        content (str): the content to save to the file
+        filename (str, optional): the name of the file. If not provided, a random name file will be created.
+    """
+    temp_dir = tempfile.gettempdir()
+    if filename is None:
+        temp_file = tempfile.NamedTemporaryFile(delete=False, dir=temp_dir)
+        filepath = temp_file.name
+    else:
+        filepath = os.path.join(temp_dir, filename)
+
+    with open(filepath, "w") as f:
+        f.write(content)
+
+    return f"File saved to {filepath}. You can read this file to process its contents."
+
+
+@tool
+def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
+    """
+    Download a file from a URL and save it to a temporary location.
+    Args:
+        url (str): the URL of the file to download.
+        filename (str, optional): the name of the file. If not provided, a random name file will be created.
+    """
+    try:
+        # Parse URL to get filename if not provided
+        if not filename:
+            path = urlparse(url).path
+            filename = os.path.basename(path)
+            if not filename:
+                filename = f"downloaded_{uuid.uuid4().hex[:8]}"
+
+        # Create temporary file
+        temp_dir = tempfile.gettempdir()
+        filepath = os.path.join(temp_dir, filename)
+
+        # Download the file
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+
+        # Save the file
+        with open(filepath, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+        return f"File downloaded to {filepath}. You can read this file to process its contents."
+    except Exception as e:
+        return f"Error downloading file: {str(e)}"
+
+
+@tool
+def extract_text_from_image(image_path: str) -> str:
+    """
+    Extract text from an image using OCR library pytesseract (if available).
+    Args:
+        image_path (str): the path to the image file.
+    """
+    try:
+        # Open the image
+        image = Image.open(image_path)
+
+        # Extract text from the image
+        text = pytesseract.image_to_string(image)
+
+        return f"Extracted text from image:\n\n{text}"
+    except Exception as e:
+        return f"Error extracting text from image: {str(e)}"
+
+
+@tool
+def analyze_csv_file(file_path: str, query: str) -> str:
+    """
+    Analyze a CSV file using pandas and answer a question about it.
+    Args:
+        file_path (str): the path to the CSV file.
+        query (str): Question about the data
+    """
+    try:
+        # Read the CSV file
+        df = pd.read_csv(file_path)
+
+        # Run various analyses based on the query
+        result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
+        result += f"Columns: {', '.join(df.columns)}\n\n"
+
+        # Add summary statistics
+        result += "Summary statistics:\n"
+        result += str(df.describe())
+
+        return result
+
+    except Exception as e:
+        return f"Error analyzing CSV file: {str(e)}"
+
+
+@tool
+def analyze_excel_file(file_path: str, query: str) -> str:
+    """
+    Analyze an Excel file using pandas and answer a question about it.
+    Args:
+        file_path (str): the path to the Excel file.
+        query (str): Question about the data
+    """
+    try:
+        # Read the Excel file
+        df = pd.read_excel(file_path)
+
+        # Run various analyses based on the query
+        result = (
+            f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
+        )
+        result += f"Columns: {', '.join(df.columns)}\n\n"
+
+        # Add summary statistics
+        result += "Summary statistics:\n"
+        result += str(df.describe())
+
+        return result
+
+    except Exception as e:
+        return f"Error analyzing Excel file: {str(e)}"
+
+
+### ============== IMAGE PROCESSING AND GENERATION TOOLS =============== ###
+import os
+import io
+import base64
+import uuid
+from PIL import Image, ImageDraw, ImageFont, ImageEnhance, ImageFilter
+
+# Helper functions for image processing
+def encode_image(image_path: str) -> str:
+    """Convert an image file to base64 string."""
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode("utf-8")
+
+
+def decode_image(base64_string: str) -> Image.Image:
+    """Convert a base64 string to a PIL Image."""
+    image_data = base64.b64decode(base64_string)
+    return Image.open(io.BytesIO(image_data))
+
+
+def save_image(image: Image.Image, directory: str = "image_outputs") -> str:
+    """Save a PIL Image to disk and return the path."""
+    os.makedirs(directory, exist_ok=True)
+    image_id = str(uuid.uuid4())
+    image_path = os.path.join(directory, f"{image_id}.png")
+    image.save(image_path)
+    return image_path
+
+@tool
+def analyze_image(image_base64: str) -> Dict[str, Any]:
+    """
+    Analyze basic properties of an image (size, mode, color analysis, thumbnail preview).
+    Args:
+        image_base64 (str): Base64 encoded image string
+    Returns:
+        Dictionary with analysis result
+    """
+    try:
+        img = decode_image(image_base64)
+        width, height = img.size
+        mode = img.mode
+
+        if mode in ("RGB", "RGBA"):
+            arr = np.array(img)
+            avg_colors = arr.mean(axis=(0, 1))
+            dominant = ["Red", "Green", "Blue"][np.argmax(avg_colors[:3])]
+            brightness = avg_colors.mean()
+            color_analysis = {
+                "average_rgb": avg_colors.tolist(),
+                "brightness": brightness,
+                "dominant_color": dominant,
+            }
+        else:
+            color_analysis = {"note": f"No color analysis for mode {mode}"}
+
+        thumbnail = img.copy()
+        thumbnail.thumbnail((100, 100))
+        thumb_path = save_image(thumbnail, "thumbnails")
+        thumbnail_base64 = encode_image(thumb_path)
+
+        return {
+            "dimensions": (width, height),
+            "mode": mode,
+            "color_analysis": color_analysis,
+            "thumbnail": thumbnail_base64,
+        }
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@tool
+def transform_image(
+    image_base64: str, operation: str, params: Optional[Dict[str, Any]] = None
+) -> Dict[str, Any]:
+    """
+    Apply transformations: resize, rotate, crop, flip, brightness, contrast, blur, sharpen, grayscale.
+    Args:
+        image_base64 (str): Base64 encoded input image
+        operation (str): Transformation operation
+        params (Dict[str, Any], optional): Parameters for the operation
+    Returns:
+        Dictionary with transformed image (base64)
+    """
+    try:
+        img = decode_image(image_base64)
+        params = params or {}
+
+        if operation == "resize":
+            img = img.resize(
+                (
+                    params.get("width", img.width // 2),
+                    params.get("height", img.height // 2),
+                )
+            )
+        elif operation == "rotate":
+            img = img.rotate(params.get("angle", 90), expand=True)
+        elif operation == "crop":
+            img = img.crop(
+                (
+                    params.get("left", 0),
+                    params.get("top", 0),
+                    params.get("right", img.width),
+                    params.get("bottom", img.height),
+                )
+            )
+        elif operation == "flip":
+            if params.get("direction", "horizontal") == "horizontal":
+                img = img.transpose(Image.FLIP_LEFT_RIGHT)
+            else:
+                img = img.transpose(Image.FLIP_TOP_BOTTOM)
+        elif operation == "adjust_brightness":
+            img = ImageEnhance.Brightness(img).enhance(params.get("factor", 1.5))
+        elif operation == "adjust_contrast":
+            img = ImageEnhance.Contrast(img).enhance(params.get("factor", 1.5))
+        elif operation == "blur":
+            img = img.filter(ImageFilter.GaussianBlur(params.get("radius", 2)))
+        elif operation == "sharpen":
+            img = img.filter(ImageFilter.SHARPEN)
+        elif operation == "grayscale":
+            img = img.convert("L")
+        else:
+            return {"error": f"Unknown operation: {operation}"}
+
+        result_path = save_image(img)
+        result_base64 = encode_image(result_path)
+        return {"transformed_image": result_base64}
+
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@tool
+def draw_on_image(
+    image_base64: str, drawing_type: str, params: Dict[str, Any]
+) -> Dict[str, Any]:
+    """
+    Draw shapes (rectangle, circle, line) or text onto an image.
+    Args:
+        image_base64 (str): Base64 encoded input image
+        drawing_type (str): Drawing type
+        params (Dict[str, Any]): Drawing parameters
+    Returns:
+        Dictionary with result image (base64)
+    """
+    try:
+        img = decode_image(image_base64)
+        draw = ImageDraw.Draw(img)
+        color = params.get("color", "red")
+
+        if drawing_type == "rectangle":
+            draw.rectangle(
+                [params["left"], params["top"], params["right"], params["bottom"]],
+                outline=color,
+                width=params.get("width", 2),
+            )
+        elif drawing_type == "circle":
+            x, y, r = params["x"], params["y"], params["radius"]
+            draw.ellipse(
+                (x - r, y - r, x + r, y + r),
+                outline=color,
+                width=params.get("width", 2),
+            )
+        elif drawing_type == "line":
+            draw.line(
+                (
+                    params["start_x"],
+                    params["start_y"],
+                    params["end_x"],
+                    params["end_y"],
+                ),
+                fill=color,
+                width=params.get("width", 2),
+            )
+        elif drawing_type == "text":
+            font_size = params.get("font_size", 20)
+            try:
+                font = ImageFont.truetype("arial.ttf", font_size)
+            except IOError:
+                font = ImageFont.load_default()
+            draw.text(
+                (params["x"], params["y"]),
+                params.get("text", "Text"),
+                fill=color,
+                font=font,
+            )
+        else:
+            return {"error": f"Unknown drawing type: {drawing_type}"}
+
+        result_path = save_image(img)
+        result_base64 = encode_image(result_path)
+        return {"result_image": result_base64}
+
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@tool
+def generate_simple_image(
+    image_type: str,
+    width: int = 500,
+    height: int = 500,
+    params: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+    """
+    Generate a simple image (gradient, noise, pattern, chart).
+    Args:
+        image_type (str): Type of image
+        width (int), height (int)
+        params (Dict[str, Any], optional): Specific parameters
+    Returns:
+        Dictionary with generated image (base64)
+    """
+    try:
+        params = params or {}
+
+        if image_type == "gradient":
+            direction = params.get("direction", "horizontal")
+            start_color = params.get("start_color", (255, 0, 0))
+            end_color = params.get("end_color", (0, 0, 255))
+
+            img = Image.new("RGB", (width, height))
+            draw = ImageDraw.Draw(img)
+
+            if direction == "horizontal":
+                for x in range(width):
+                    r = int(
+                        start_color[0] + (end_color[0] - start_color[0]) * x / width
+                    )
+                    g = int(
+                        start_color[1] + (end_color[1] - start_color[1]) * x / width
+                    )
+                    b = int(
+                        start_color[2] + (end_color[2] - start_color[2]) * x / width
+                    )
+                    draw.line([(x, 0), (x, height)], fill=(r, g, b))
+            else:
+                for y in range(height):
+                    r = int(
+                        start_color[0] + (end_color[0] - start_color[0]) * y / height
+                    )
+                    g = int(
+                        start_color[1] + (end_color[1] - start_color[1]) * y / height
+                    )
+                    b = int(
+                        start_color[2] + (end_color[2] - start_color[2]) * y / height
+                    )
+                    draw.line([(0, y), (width, y)], fill=(r, g, b))
+
+        elif image_type == "noise":
+            noise_array = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
+            img = Image.fromarray(noise_array, "RGB")
+
+        else:
+            return {"error": f"Unsupported image_type {image_type}"}
+
+        result_path = save_image(img)
+        result_base64 = encode_image(result_path)
+        return {"generated_image": result_base64}
+
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@tool
+def combine_images(
+    images_base64: List[str], operation: str, params: Optional[Dict[str, Any]] = None
+) -> Dict[str, Any]:
+    """
+    Combine multiple images (collage, stack, blend).
+    Args:
+        images_base64 (List[str]): List of base64 images
+        operation (str): Combination type
+        params (Dict[str, Any], optional)
+    Returns:
+        Dictionary with combined image (base64)
+    """
+    try:
+        images = [decode_image(b64) for b64 in images_base64]
+        params = params or {}
+
+        if operation == "stack":
+            direction = params.get("direction", "horizontal")
+            if direction == "horizontal":
+                total_width = sum(img.width for img in images)
+                max_height = max(img.height for img in images)
+                new_img = Image.new("RGB", (total_width, max_height))
+                x = 0
+                for img in images:
+                    new_img.paste(img, (x, 0))
+                    x += img.width
+            else:
+                max_width = max(img.width for img in images)
+                total_height = sum(img.height for img in images)
+                new_img = Image.new("RGB", (max_width, total_height))
+                y = 0
+                for img in images:
+                    new_img.paste(img, (0, y))
+                    y += img.height
+        else:
+            return {"error": f"Unsupported combination operation {operation}"}
+
+        result_path = save_image(new_img)
+        result_base64 = encode_image(result_path)
+        return {"combined_image": result_base64}
+
+    except Exception as e:
+        return {"error": str(e)}
+
+
+tools = [multiply, add, subtract, divide, wikidata_search, tavily_search_tool,combine_images, analyze_image, transform_image, draw_on_image, generate_simple_image, analyze_csv_file, analyze_excel_file, save_and_read_file, download_file_from_image, extract_text_from_image]
 
 def build_graph():
     llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", api_key=os.getenv("GOOGLE_API_KEY"))