feat: image_generation and best model for task tools
Browse files- .gitignore +0 -0
- app.py +74 -55
- requirements.txt +11 -2
- tools/best_model_for_task.py +22 -0
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app.py
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import datetime
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import requests
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import pytz
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import yaml
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import pycountry
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from tools.final_answer import FinalAnswerTool
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from tools.visit_webpage import VisitWebpageTool
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from tools.translation import TranslationTool
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from transformers import pipeline
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from Gradio_UI import GradioUI
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from typing import Optional
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import os
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import base64
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from opentelemetry.sdk.trace import TracerProvider
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from openinference.instrumentation.smolagents import SmolagentsInstrumentor
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from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
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from opentelemetry.sdk.trace.export import SimpleSpanProcessor
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from smolagents import (
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CodeAgent,
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DuckDuckGoSearchTool,
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GoogleSearchTool,
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HfApiModel,
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load_tool,
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)
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@tool
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def get_current_time_in_timezone(timezone: str) -> str:
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"""A tool that fetches the current local time in a specified timezone
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Args:
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timezone (str): A string representing a valid timezone (e.g., 'America/New_York').
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"""
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try:
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# Create timezone object
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tz = pytz.timezone(timezone)
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local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
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return f"The current local time in {timezone} is: {local_time}"
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except Exception as e:
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return f"Error fetching time for timezone '{timezone}': {str(e)}"
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@tool
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def conversational_utterance(user_content: str, additional_context: Optional[str]="") -> str:
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"""
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A tool that replies to a single casual query or message triggering any other tool is unfitted to reply.
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Args:
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user_content: A string with the user's message or query (e.g., "Hi!", "How are you?", "Tell me a joke").
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additional_context: An optional string with additional information (such as context, metadata, conversation history,
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or instructions) to be passed as an 'assistant' turn (a thought) in the conversation.
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"""
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system_context_message = f"""
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You are a highly intelligent, expert, and witty assistant who responds to user conversational messages.
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You function as a tool activated by user intention via AI agents. In addition to your native LLM capabilities,
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you have access to the following system tools that the user may leverage:
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{tools}
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You should mention these tools whenever relevant during the conversation.
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"""
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messages = [
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{"role": "system", "content": [{"type": "text", "text": system_context_message}]},
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{"role": "assistant", "content": [{"type": "text", "text": f"(additional_context: {additional_context})"}]},
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{"role": "user", "content": [{"type": "text", "text": user_content}]}
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]
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return model(messages).content
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@tool
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def language_detection(text:str)-> str:
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return "None"
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def initialize_langfuse_opentelemetry_instrumentation():
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LANGFUSE_PUBLIC_KEY=os.environ.get("LANGFUSE_PUBLIC_KEY")
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LANGFUSE_SECRET_KEY=os.environ.get("LANGFUSE_SECRET_KEY")
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initialize_langfuse_opentelemetry_instrumentation()
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# tools from /tools/
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final_answer = FinalAnswerTool()
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visit_webpage = VisitWebpageTool()
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# tools from Hub
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image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
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model = HfApiModel(
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max_tokens=2096,
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temperature=0.5,
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model_id='
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custom_role_conversions=None,
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)
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# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
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# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'
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with open("prompts.yaml", 'r') as stream:
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prompt_templates = yaml.safe_load(stream)
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tools = [
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final_answer,
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visit_webpage,
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get_current_time_in_timezone,
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image_generation_tool,
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language_detection,
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]
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agent = CodeAgent(
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model=model,
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tools=tools,
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max_steps=
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verbosity_level=
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grammar=None,
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planning_interval=None,
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name=None,
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from datetime import datetime
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import pytz
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import yaml
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import pycountry
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from tools.final_answer import FinalAnswerTool
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from tools.visit_webpage import VisitWebpageTool
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from tools.translation import TranslationTool
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from tools.best_model_for_task import HFModelDownloadsTool
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from transformers import pipeline
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from Gradio_UI import GradioUI
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import os
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import base64
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from dotenv import load_dotenv
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from opentelemetry.sdk.trace import TracerProvider
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from openinference.instrumentation.smolagents import SmolagentsInstrumentor
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from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
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from opentelemetry.sdk.trace.export import SimpleSpanProcessor
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from langchain_community.agent_toolkits.load_tools import load_tools
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from langchain.chains import LLMChain
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from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper
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from langchain_core.prompts import PromptTemplate
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from langchain_openai import OpenAI
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from skimage import io
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from PIL import Image
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from smolagents import (
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CodeAgent,
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DuckDuckGoSearchTool,
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GoogleSearchTool,
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HfApiModel,
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TransformersModel,
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load_tool,
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Tool,
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tool,
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ToolCollection
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)
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# load .env vars
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load_dotenv()
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# fast prototyping tools
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@tool
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def get_current_time_in_timezone(timezone: str) -> str:
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"""A tool that fetches the current local time in a specified timezone formatted as '%m/%d/%y %H:%M:%S'
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Args:
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timezone (str): A string representing a valid timezone (e.g., 'America/New_York').
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"""
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try:
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tz = pytz.timezone(timezone)
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local_time = datetime.now(tz).strftime('%m/%d/%y %H:%M:%S')
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return f"The current local time in {timezone} is: {local_time}"
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except Exception as e:
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return f"Error fetching time for timezone '{timezone}': {str(e)}"
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@tool
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def language_detection(text:str)-> str:
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return "None"
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@tool
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def advanced_image_generation(description:str)->Image.Image:
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"""Generates an image using a textual description.
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Args:
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description: the textual description provided by the user to prompt a text-to-image model
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"""
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llm = OpenAI(temperature=0.9)
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prompt = PromptTemplate(
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input_variables=["image_desc"],
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template="Generate a detailed but short prompt (must be less than 900 characters) to generate an image based on the following description: {image_desc}",
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)
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chain = LLMChain(llm=llm, prompt=prompt)
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image_url = DallEAPIWrapper().run(chain.run(description))
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image_array = io.imread(image_url)
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pil_image = Image.fromarray(image_array)
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return pil_image
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# telemetry
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def initialize_langfuse_opentelemetry_instrumentation():
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LANGFUSE_PUBLIC_KEY=os.environ.get("LANGFUSE_PUBLIC_KEY")
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LANGFUSE_SECRET_KEY=os.environ.get("LANGFUSE_SECRET_KEY")
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initialize_langfuse_opentelemetry_instrumentation()
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# load tools from /tools/
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final_answer = FinalAnswerTool()
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visit_webpage = VisitWebpageTool()
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translation = TranslationTool()
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best_model_for_task = HFModelDownloadsTool()
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# load tools from smoloagents library
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google_web_search = GoogleSearchTool()
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google_web_search.name = "google_web_search"
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duckduckgo_web_search = DuckDuckGoSearchTool()
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duckduckgo_web_search.name = "duckduckgo_web_search"
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# load tools from hub and langchain
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# image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
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image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True) # Tool.from_space("black-forest-labs/FLUX.1-schnell", name="image_generator", description="Generate an image from a prompt")
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advanced_search_tool = Tool.from_langchain(load_tools(["searchapi"], allow_dangerous_tools=True)[0]) # serpapi is not real time scrapping
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advanced_search_tool.name = "advanced_search_tool"
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image_generation_tool_fast = Tool.from_space(
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"black-forest-labs/FLUX.1-schnell",
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name="image_generator",
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description="Generate an image from a prompt"
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)
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# alternative hf inference endpoint
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model = HfApiModel(
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max_tokens=2096,
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temperature=0.5,
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model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud',
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custom_role_conversions=None,
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)
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with open("prompts.yaml", 'r') as stream:
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prompt_templates = yaml.safe_load(stream)
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tools = [
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final_answer,
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best_model_for_task,
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advanced_search_tool,
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google_web_search,
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duckduckgo_web_search,
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visit_webpage,
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get_current_time_in_timezone,
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advanced_image_generation,
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image_generation_tool,
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language_detection,
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translation
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]
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agent = CodeAgent(
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model=model,
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tools=tools,
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max_steps=10,
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verbosity_level=1,
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grammar=None,
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planning_interval=None,
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name=None,
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requirements.txt
CHANGED
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duckduckgo_search
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pandas
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transformers
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torch
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pycountry
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opentelemetry-sdk
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opentelemetry-exporter-otlp
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openinference-instrumentation-smolagents
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duckduckgo_search
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pandas
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transformers
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# transformers[agents]
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torch
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langchain
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openai
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# accelerate
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langchain-community
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google-search-results
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smolagents[transformers]
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scikit-image
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pycountry
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opentelemetry-sdk
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opentelemetry-exporter-otlp
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openinference-instrumentation-smolagents
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python-dotenv
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langchain-openai
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tools/best_model_for_task.py
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from smolagents import Tool
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class HFModelDownloadsTool(Tool):
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name = "model_download_counter"
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description = """
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This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub.
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It returns the name of the checkpoint."""
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inputs = {
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"task": {
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"type": "string",
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"description": "the task category (such as text-classification, depth-estimation, etc)",
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}
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}
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output_type = "string"
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def forward(self, task: str):
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from huggingface_hub import list_models
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model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
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return model.id
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model_downloads_tool = HFModelDownloadsTool()
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