Delete tools/tools/multimodal_tools.py

tools/tools/multimodal_tools.py

@@ -1,177 +0,0 @@
-import base64
-import os
-from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.tools import Tool
-from langchain_core.tools import tool
-
-api_key = os.getenv("GEMINI_API_KEY")
-
-# Create LLM class
-vision_llm = ChatGoogleGenerativeAI(
-    model= "gemini-2.5-flash-preview-05-20",
-    temperature=0,
-    max_retries=2,
-    google_api_key=api_key
-)
-
-@tool("extract_text_tool", parse_docstring=True)
-def extract_text(img_path: str) -> str:
-    """Extract text from an image file using a multimodal model.
-
-    Args:
-        img_path (str): The path to the image file from which to extract text.
-    
-    Returns:
-        str: The extracted text from the image, or an empty string if an error occurs.
-    """
-    all_text = ""
-    try:
-        # Read image and encode as base64
-        with open(img_path, "rb") as image_file:
-            image_bytes = image_file.read()
-
-        image_base64 = base64.b64encode(image_bytes).decode("utf-8")
-
-        # Prepare the prompt including the base64 image data
-        message = [
-            HumanMessage(
-                content=[
-                    {
-                        "type": "text",
-                        "text": (
-                            "Extract all the text from this image. "
-                            "Return only the extracted text, no explanations."
-                        ),
-                    },
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:image/png;base64,{image_base64}"
-                        },
-                    },
-                ]
-            )
-        ]
-
-        # Call the vision-capable model
-        response = vision_llm.invoke(message)
-
-        # Append extracted text
-        all_text += response.content + "\n\n"
-
-        return all_text.strip()
-    except Exception as e:
-        # A butler should handle errors gracefully
-        error_msg = f"Error extracting text: {str(e)}"
-        print(error_msg)
-        return ""
-    
-@tool("analyze_image_tool", parse_docstring=True)
-def analyze_image_tool(user_query: str, img_path: str) -> str:
-    """Answer the question reasoning on the image.
-    
-    Args:
-        user_query (str): The question to be answered based on the image.
-        img_path (str): Path to the image file to be analyzed.
-
-    Returns:
-        str: The answer to the query based on image content, or an empty string if an error occurs.
-    """
-    all_text = ""
-    try:
-        # Read image and encode as base64
-        with open(img_path, "rb") as image_file:
-            image_bytes = image_file.read()
-
-        image_base64 = base64.b64encode(image_bytes).decode("utf-8")
-
-        # Prepare the prompt including the base64 image data
-        message = [
-            HumanMessage(
-                content=[
-                    {
-                        "type": "text",
-                        "text": (
-                            f"User query: {user_query}"
-                        ),
-                    },
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:image/png;base64,{image_base64}"
-                        },
-                    },
-                ]
-            )
-        ]
-
-        # Call the vision-capable model
-        response = vision_llm.invoke(message)
-
-        # Append extracted text
-        all_text += response.content + "\n\n"
-
-        return all_text.strip()
-    except Exception as e:
-        # A butler should handle errors gracefully
-        error_msg = f"Error analyzing image: {str(e)}"
-        print(error_msg)
-        return ""
-    
-@tool("analyze_audio_tool", parse_docstring=True)
-def analyze_audio_tool(user_query: str, audio_path: str) -> str:
-    """Answer the question by reasoning on the provided audio file.
-    
-    Args:
-        user_query (str): The question to be answered based on the audio content.
-        audio_path (str): Path to the audio file (e.g., .mp3, .wav, .flac, .aac, .ogg).
-
-    Returns:
-        str: The answer to the query based on audio content, or an error message/empty string if an error occurs.
-    """
-    try:
-        # Determine MIME type from file extension
-        _filename, file_extension = os.path.splitext(audio_path)
-        file_extension = file_extension.lower()
-
-        supported_formats = {
-            ".mp3": "audio/mp3", ".wav": "audio/wav", ".flac": "audio/flac",
-            ".aac": "audio/aac", ".ogg": "audio/ogg"
-        }
-
-        if file_extension not in supported_formats:
-            return (f"Error: Unsupported audio file format '{file_extension}'. "
-                    f"Supported extensions: {', '.join(supported_formats.keys())}.")
-        mime_type = supported_formats[file_extension]
-
-        # Read audio file and encode as base64
-        with open(audio_path, "rb") as audio_file:
-            audio_bytes = audio_file.read()
-        audio_base64 = base64.b64encode(audio_bytes).decode("utf-8")
-
-        # Prepare the prompt including the base64 audio data
-        message = [ 
-        HumanMessage(
-            content=[
-                {
-                    "type": "text",
-                    "text": f"User query: {user_query}",
-                },
-                {
-                    "type": "audio",
-                    "source_type": "base64",
-                    "mime_type": mime_type,
-                    "data": audio_base64
-                },
-            ]
-        )
-        ]
-
-        # Call the vision-capable model
-        response = vision_llm.invoke(message)
-        return response.content.strip()
-    except Exception as e:
-        error_msg = f"Error analyzing audio: {str(e)}"
-        print(error_msg)
-        return ""