update

agent_tools.py

@@ -1,215 +0,0 @@
-# %%
-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
-
-# Load environment variables
-load_dotenv()
-
-# 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."
-)
-
-# 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."
-)
-
-# REVERSE TOOLS
-
-
-def ReverseTextTool(text: str) -> str:
-    """Reverses the order of characters in a given text string."""
-    try:
-        return text[::-1]
-    except Exception as e:
-        return f"Error reversing text: {str(e)}"
-
-
-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."
-)
-
-# DOWNLOAD A FILE
-
-
-def download_file(url: str) -> str:
-    """Downloads a file from a given URL and returns the local file path."""
-    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
-    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 and returns the local file path."
-)
-
-# DEFINE IMAGE RECOGNITION TOOLS
-
-
-def create_vision_llm():
-    """Creates a vision-capable LLM with proper error handling."""
-    try:
-        # Check if OpenAI API key is available
-        if not os.getenv("OPENAI_API_KEY"):
-            return None, "OpenAI API key not found. Please set OPENAI_API_KEY in your environment variables."
-
-        vision_llm = ChatOpenAI(model="gpt-4o")
-        return vision_llm, None
-    except Exception as e:
-        return None, f"Error creating vision LLM: {str(e)}"
-
-
-def image_recognition(img_path: str) -> str:
-    """Analyzes and describes the content of images using AI vision."""
-    try:
-        # Check if file exists
-        if not os.path.exists(img_path):
-            return f"Error: Image file not found at {img_path}"
-
-        # Create vision LLM
-        vision_llm, error = create_vision_llm()
-        if error:
-            return error
-
-        # 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 or 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)
-        return response.content.strip()
-
-    except Exception as e:
-        return f"Error analyzing image: {str(e)}"
-
-
-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 functions (commented out to avoid side effects)
-
-
-def test_tools():
-    """Test all tools to ensure they work properly."""
-    print("Testing Hub Stats Tool:")
-    print(hub_stats_tool.invoke("google"))
-    print("\n" + "="*50 + "\n")
-
-    print("Testing Web Search Tool:")
-    results = web_search_tool.invoke("what is the matrix?")
-    pp = pprint.PrettyPrinter()
-    print(pp.pprint(results))
-    print("\n" + "="*50 + "\n")
-
-    print("Testing Reverse Text Tool:")
-    results = reverse_text_tool.invoke("what is the matrix?")
-    print(results)
-    print("\n" + "="*50 + "\n")
-
-    print("Testing Download File Tool:")
-    test_url = "https://www.google.com"
-    results = download_file_tool.invoke(test_url)
-    print(results)
-    print("\n" + "="*50 + "\n")
-
-    print("Testing Image Recognition Tool:")
-    test_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg"
-    downloaded_file = download_file_tool.invoke(test_url)
-    if not downloaded_file.startswith("Failed"):
-        results = image_recognition_tool.invoke(downloaded_file)
-        print(results)
-    else:
-        print("Skipping image recognition test due to download failure")
-
-# Uncomment the line below to run tests
-# test_tools()

app.py

@@ -9,7 +9,7 @@ from langgraph.graph import START, StateGraph
 from langgraph.prebuilt import ToolNode, tools_condition
 from langgraph.graph.message import add_messages
 from typing import TypedDict, Annotated
-from agent_tools import image_recognition_tool, download_file_tool, reverse_text_tool, hub_stats_tool, web_search_tool
+from tools import image_recognition_tool, download_file_tool, reverse_text_tool, hub_stats_tool, web_search_tool
 
 # (Keep Constants as is)
 # --- Constants ---