app.py

from doctest import debug

import yaml
from smolagents import (
    load_tool,
    CodeAgent,
    HfApiModel,
    VisitWebpageTool,
)

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

from openinference.instrumentation.smolagents import SmolagentsInstrumentor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

from gradio_ui import GradioUI

endpoint = "http://0.0.0.0:6006/v1/traces"
trace_provider = TracerProvider()
trace_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))

SmolagentsInstrumentor().instrument(tracer_provider=trace_provider)

model_id='Qwen/Qwen2.5-Coder-32B-Instruct'
# model_id='meta-llama/Llama-3.3-70B-Instruct'
model = HfApiModel(
max_tokens=200,
temperature=0.5,
model_id=model_id,
custom_role_conversions=None,
)


# Import tool from Hub
image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True)

model = HfApiModel(model_id)

visitWebpageTool = VisitWebpageTool()

with open("prompts.yaml", 'r') as stream:
    prompt_templates = yaml.safe_load(stream)


examples = [
    [{"text": "what are AI agents ?"}],
    [{"text": "how are tools used and how do they fit in ?"}],
    [{"text": "What do you know about tokenization"}],
    [{"text": "What do you know about ReAct ?"}],
    [{"text": "What are special tokens ?"}],
    [{"text": "What are actions ?"}],
    [{"text": "What is the role of an LLM ?"}],
    [{"text": "What are some of the topics covered in the course ?"}],
]

# Initialize the agent with the image generation tool
agent = CodeAgent(tools=[visitWebpageTool, image_generation_tool], model=model, prompt_templates=prompt_templates)

gr = GradioUI(agent).launch()
# Loop over each example and call agent.run() with the text
# for prompt in prompts:
#     text = prompt[0]["text"]
#     response = agent.run(text)
#     print(f"Input: {text}\nResponse: {response}\n")



# messages = gr.State([])
# messages.append(gr.ChatMessage(role="assistant", content="testing"))