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5ace4e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | import os
import gradio as gr
from dotenv import load_dotenv
from strands import Agent
from strands.models.litellm import LiteLLMModel
from mcp.client.streamable_http import streamablehttp_client
from strands.tools.mcp.mcp_client import MCPClient
load_dotenv()
os.environ["STRANDS_TOOL_CONSOLE_MODE"] = "enabled"
def create_streamable_http_transport():
return streamablehttp_client(os.environ["MCP_SERVER"])
streamable_http_mcp_client = MCPClient(create_streamable_http_transport)
model = LiteLLMModel(
client_args={
"api_key": os.environ["OPENROUTER_API_KEY"],
"api_base": "https://openrouter.ai/api/v1",
},
model_id="openrouter/google/gemini-2.5-flash"
)
SYSTEM_PROMPT = """
You are an Image Agent that allows users to generate an image using prompt or edit images
using prompt. Always render the images as markdown
"""
def convert_history(history):
"""
Convert Gradio 6 history format:
[{"role": "...", "content": [{"text": "..."}]}]
Into Required format:
[{"role": "...", "content": "..."}]
"""
messages = []
for msg in history:
# Gradio packs content into list of content blocks
if isinstance(msg["content"], list):
# extract just the text pieces
text = "".join(block.get("text", "") for block in msg["content"])
else:
text = msg["content"]
messages.append({"role": msg["role"], "content": text})
return messages
def response_generator(message, history):
"""
Response Generator
"""
messages = convert_history(history) if history else []
agent_messages = []
for msg in messages:
agent_messages.append({
"role": msg["role"],
"content": [{"text": msg["content"]}]
})
with streamable_http_mcp_client:
# Get the tools from the MCP server
tools = streamable_http_mcp_client.list_tools_sync()
image_agent = Agent(
model=model,
system_prompt=SYSTEM_PROMPT,
tools=tools,
messages=agent_messages
)
print(agent_messages)
messages.append({"role": "user", "content": message})
response = image_agent(message)
response = response.message["content"][0]["text"]
response = response.replace("sandbox:/", "")
yield response
demo = gr.ChatInterface(
fn=response_generator,
examples=[["A scenic landscape with mountains, a river, and a clear blue sky", None]],
title="Image Agent"
)
demo.launch()
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