Create app.py

app.py

@@ -0,0 +1,88 @@
+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()