added logs to gradio app.

app.py

@@ -1,3 +1,5 @@
+import contextlib
+import io
 import os
 import tempfile
 
@@ -7,50 +9,61 @@ from PIL import Image
 from agents import run_photo_enchancement_agent
 
 
-def process_image_with_agents(image, prompt):
-    # Save uploaded image to a temp file
+def process_image_with_agents(image: Image.Image, prompt: str):
+    """Stream intermediate steps **and** the agent's stdout / stderr logs."""
+    # 🔧 1. Create temp dir & paths
     temp_dir = tempfile.mkdtemp(prefix="gradio_aiart_")
     input_path = os.path.join(temp_dir, "input.jpg")
+    output_path = os.path.join(temp_dir, "output.jpg")
+
+    # 💾 2. Persist original upload
     image.save(input_path)
 
-    output_path = os.path.join(temp_dir, "output.jpg")
+    # 🖼️ 3. Yield the original image immediately
+    yield image, "Original image uploaded. Starting enhancement…"
 
-    # Simulate streaming: first yield original image
-    yield [[input_path], ["Original"], image]
+    # 📝 4. Capture logs while the agent runs
+    log_buffer = io.StringIO()
+    with contextlib.redirect_stdout(log_buffer), contextlib.redirect_stderr(log_buffer):
+        _ = run_photo_enchancement_agent(
+            prompt,
+            image_path=input_path,
+            output_path=output_path,
+        )
 
-    # Run the enhancement agent
-    _ = run_photo_enchancement_agent(prompt, image_path=input_path, output_path=output_path)
+    # 🧾 All logs produced by the agent
+    logs = log_buffer.getvalue()
 
-    # Load the final image
+    # 🖼️ 5. Yield the final image plus the complete logs
     final_image = Image.open(output_path)
+    yield final_image, f"✅ Enhancement finished.\n\n--- Agent Logs ---\n{logs}"
 
-    # Stream final result
-    yield [[input_path, output_path], ["Original", "Final (after agent workflow)"], final_image]
 
-
-with gr.Blocks(title="AI Art Director : Agent Workflow") as demo:
+with gr.Blocks(title="AI Art Director • Agent Workflow") as demo:
     gr.Markdown(
-        "#  AI Art Director : Agent Workflow\n"
-        "Upload an image and describe the vibe you want. "
-        "AI agents will propose, apply, and critique edits to match your vision."
+        "# AI Art Director\n"
+        "Upload an image and describe the vibe you want.\n"
+        "The agent will propose, apply, and critique edits to match your vision "
+        "– and you'll see progress **and logs** live!"
     )
+
     with gr.Row():
         with gr.Column():
             image_input = gr.Image(type="pil", label="Upload Image")
-            prompt_input = gr.Textbox(
-                label="Describe the vibe you want", placeholder="e.g. dreamy, vintage, vibrant..."
-            )
+            prompt_input = gr.Textbox(label="Describe the vibe you want", placeholder="e.g. dreamy, vintage, vibrant…")
             submit_btn = gr.Button("Go!")
         with gr.Column():
-            gallery = gr.Gallery(label="Workflow Images", show_label=True)
-            critiques = gr.HighlightedText(label="Agent Comments", show_label=True)
-            final = gr.Image(label="Final Image", show_label=True)
+            streamed_image = gr.Image(label="Image Progress")
+            agent_logs = gr.Textbox(label="Agent Logs", lines=18, interactive=False)
 
     submit_btn.click(
         process_image_with_agents,
         inputs=[image_input, prompt_input],
-        outputs=[gallery, critiques, final],
+        outputs=[streamed_image, agent_logs],
     )
 
+    # Allow multiple users to queue without blocking streaming
+    demo.queue()
+
 if __name__ == "__main__":
     demo.launch()