new tools

tools.py

@@ -0,0 +1,172 @@
+from io import BytesIO
+import requests
+from PIL import Image as PILImage
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
+from huggingface_hub import list_models
+import random
+import pprint
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+from langgraph.prebuilt import tools_condition
+from langgraph.graph import START, StateGraph
+from IPython.display import Image, display
+
+from langgraph.prebuilt import ToolNode
+from langchain_core.messages import AnyMessage, HumanMessage, AIMessage, SystemMessage
+from langgraph.graph.message import add_messages
+from typing import TypedDict, Annotated
+from langchain.tools import Tool
+from langchain_community.retrievers import BM25Retriever
+from langchain.docstore.document import Document
+import datasets
+from langchain_openai import ChatOpenAI
+from dotenv import load_dotenv
+import os
+import torch
+import base64
+
+# DEFINE HUB STAT TOOLS
+
+
+def get_hub_stats(author: str) -> str:
+    """Fetches the most downloaded model from a specific author on the Hugging Face Hub."""
+    try:
+        # List models from the specified author, sorted by downloads
+        models = list(list_models(
+            author=author, sort="downloads", direction=-1, limit=1))
+
+        if models:
+            model = models[0]
+            return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads."
+        else:
+            return f"No models found for author {author}."
+    except Exception as e:
+        return f"Error fetching models for {author}: {str(e)}"
+
+
+# Initialize the tool
+hub_stats_tool = Tool(
+    name="get_hub_stats",
+    func=get_hub_stats,
+    description="Search HuggingFace Hub for model statistics, downloads, and author information. Use this when asking about specific models, authors, or HuggingFace Hub data."
+)
+
+print(hub_stats_tool.invoke("google"))
+
+# DEFINE WEB SEARCH TOOLS
+web_search_tool = Tool(
+    name="search_tool",
+    func=DuckDuckGoSearchRun(),
+    description="Search the general web for current information, news, and general knowledge. Use this for questions about companies, people, events, etc."
+)
+
+results = web_search_tool.invoke("what is the matrix?")
+pp = pprint.PrettyPrinter()
+print(pp.pprint(results))
+
+
+# REVERSE TOOLS
+def ReverseTextTool(text: str) -> str:
+    return text[::-1]
+
+
+reverse_text_tool = Tool(
+    name="reverse_text_tool",
+    func=ReverseTextTool,
+    description="Reverses the order of characters in a given text string. Use this when you need to reverse text."
+)
+
+results = reverse_text_tool.invoke("what is the matrix?")
+pp = pprint.PrettyPrinter()
+print(pp.pprint(results))
+
+# DOWNLOAD A FILE
+
+
+def download_file(url: str) -> str:
+    try:
+        response = requests.get(url, timeout=30)
+        response.raise_for_status()
+
+        # Define save_path - extract filename from URL
+        filename = url.split(
+            '/')[-1] if url.split('/')[-1] else 'downloaded_file'
+        save_path = f"./{filename}"
+
+        with open(save_path, "wb") as f:
+            f.write(response.content)
+        return save_path  # Return the file path instead of success message
+    except Exception as e:
+        return f"Failed to download: {e}"
+
+
+download_file_tool = Tool(
+    name="download_file_tool",
+    func=download_file,
+    description="Downloads a file from a given URL."
+)
+
+results = download_file_tool.invoke("https://www.google.com")
+print(results)
+
+# DEFINE IMAGE RECOGNITION TOOLS
+vision_llm = ChatOpenAI(model="gpt-4o")
+
+
+def image_recognition(img_path: str) -> str:
+
+    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": (
+                            "Describe the image or extract all the text from this image. "
+                            "Return only the description orextracted 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 ""
+
+
+image_recognition_tool = Tool(
+    name="image_recognition_tool",
+    func=image_recognition,
+    description="Analyzes and describes the content of images using AI vision. Use this when you need to understand what's in an image."
+)
+test_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg"
+
+results = image_recognition_tool.invoke(download_file_tool.invoke(test_url))
+print(results)
+
+#