Improve visualizer output when invalid auth token is provided (#140)
This commit is contained in:
Suchintan
@@ -256,186 +256,202 @@ with visualizer_tab:
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col_steps.markdown(f"#### Steps")
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col_steps.markdown(f"#### Steps")
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col_artifacts.markdown("#### Artifacts")
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col_artifacts.markdown("#### Artifacts")
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tasks_response = repository.get_tasks(task_page_number)
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tasks_response = repository.get_tasks(task_page_number)
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if "error" in tasks_response:
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if type(tasks_response) is not list:
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st.write(tasks_response)
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st.error("Failed to fetch tasks.")
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st.error(tasks_response)
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# Display tasks in sidebar for selection
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if "Organization not found" in str(tasks_response) or "Could not validate credentials" in str(tasks_response):
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tasks = {task["task_id"]: task for task in tasks_response}
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st.error("Please check the organization credentials in .streamlit/secrets.toml.")
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task_id_buttons = {
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st.error(
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task_id: col_tasks.button(
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"You can validate the credentials against the postgresql credentials by running\n\n"
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f"{task_id}",
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'`psql -U skyvern -h localhost -d skyvern -c "SELECT o.organization_id, o.organization_name, token FROM organizations o JOIN organization_auth_tokens oat ON oat.organization_id = o.organization_id;"`.'
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on_click=select_task,
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"\n\n NOTE: There might be multiple organizations -- each run of ./setup.sh creates a new one. Pick your favourite!"
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args=(task,),
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use_container_width=True,
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type="primary" if selected_task and task_id == selected_task["task_id"] else "secondary",
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)
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for task_id, task in tasks.items()
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}
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# Display pagination buttons
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task_page_prev, _, show_task_page_number, _, task_page_next = col_tasks.columns([1, 1, 1, 1, 1])
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show_task_page_number.button(str(task_page_number), disabled=True)
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if task_page_next.button("\>"):
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st.session_state.task_page_number += 1
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if task_page_prev.button("\<", disabled=task_page_number == 1):
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st.session_state.task_page_number = max(1, st.session_state.task_page_number - 1)
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(
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tab_task,
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tab_step,
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tab_recording,
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tab_screenshot,
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tab_post_action_screenshot,
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tab_id_to_xpath,
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tab_element_tree,
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tab_element_tree_trimmed,
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tab_llm_prompt,
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tab_llm_request,
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tab_llm_response_parsed,
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tab_llm_response_raw,
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tab_html,
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) = col_artifacts.tabs(
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[
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":green[Task]",
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":blue[Step]",
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":violet[Recording]",
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":rainbow[Screenshot]",
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":rainbow[Action Screenshots]",
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":red[ID -> XPath]",
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":orange[Element Tree]",
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":blue[Element Tree (Trimmed)]",
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":yellow[LLM Prompt]",
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":green[LLM Request]",
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":blue[LLM Response (Parsed)]",
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":violet[LLM Response (Raw)]",
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":rainbow[Html (Raw)]",
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]
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)
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tab_task_details, tab_task_steps, tab_task_action_results = tab_task.tabs(["Details", "Steps", "Action Results"])
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if selected_task:
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tab_task_details.json(selected_task)
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if selected_task_recording_uri:
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streamlit_show_recording(tab_recording, selected_task_recording_uri)
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if task_steps:
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col_steps_prev, _, col_steps_next = col_steps.columns([3, 1, 3])
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col_steps_prev.button(
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"prev", on_click=go_to_previous_step, key="previous_step_button", use_container_width=True
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)
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)
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col_steps_next.button("next", on_click=go_to_next_step, key="next_step_button", use_container_width=True)
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step_id_buttons = {
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else:
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step["step_id"]: col_steps.button(
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# Display tasks in sidebar for selection
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f"{step['order']} - {step['retry_index']} - {step['step_id']}",
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tasks = {task["task_id"]: task for task in tasks_response}
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on_click=select_step,
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task_id_buttons = {
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args=(step,),
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task_id: col_tasks.button(
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f"{task_id}",
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on_click=select_task,
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args=(task,),
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use_container_width=True,
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use_container_width=True,
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type="primary" if selected_step and step["step_id"] == selected_step["step_id"] else "secondary",
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type="primary" if selected_task and task_id == selected_task["task_id"] else "secondary",
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)
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)
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for step in task_steps
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for task_id, task in tasks.items()
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}
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}
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df = pd.json_normalize(task_steps)
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# Display pagination buttons
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tab_task_steps.dataframe(df, use_container_width=True, height=1000)
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task_page_prev, _, show_task_page_number, _, task_page_next = col_tasks.columns([1, 1, 1, 1, 1])
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show_task_page_number.button(str(task_page_number), disabled=True)
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if task_page_next.button("\>"):
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st.session_state.task_page_number += 1
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if task_page_prev.button("\<", disabled=task_page_number == 1):
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st.session_state.task_page_number = max(1, st.session_state.task_page_number - 1)
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task_action_results = []
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(
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for step in task_steps:
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tab_task,
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output = step.get("output")
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tab_step,
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step_id = step["step_id"]
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tab_recording,
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if output:
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tab_screenshot,
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step_action_results = output.get("action_results", [])
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tab_post_action_screenshot,
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for action_result in step_action_results:
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tab_id_to_xpath,
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task_action_results.append(
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tab_element_tree,
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{
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tab_element_tree_trimmed,
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"step_id": step_id,
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tab_llm_prompt,
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"order": step["order"],
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tab_llm_request,
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"retry_index": step["retry_index"],
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tab_llm_response_parsed,
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**action_result,
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tab_llm_response_raw,
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}
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tab_html,
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)
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) = col_artifacts.tabs(
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df = pd.json_normalize(task_action_results)
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[
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df = df.reindex(sorted(df.columns), axis=1)
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":green[Task]",
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tab_task_action_results.dataframe(df, use_container_width=True, height=1000)
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":blue[Step]",
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":violet[Recording]",
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":rainbow[Screenshot]",
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":rainbow[Action Screenshots]",
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":red[ID -> XPath]",
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":orange[Element Tree]",
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":blue[Element Tree (Trimmed)]",
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":yellow[LLM Prompt]",
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":green[LLM Request]",
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":blue[LLM Response (Parsed)]",
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":violet[LLM Response (Raw)]",
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":rainbow[Html (Raw)]",
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]
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)
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if selected_step:
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tab_task_details, tab_task_steps, tab_task_action_results = tab_task.tabs(
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tab_step.json(selected_step)
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["Details", "Steps", "Action Results"]
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)
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artifacts_response = repository.get_artifacts(selected_task["task_id"], selected_step["step_id"])
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if selected_task:
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split_artifact_uris = [artifact["uri"].split("/") for artifact in artifacts_response]
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tab_task_details.json(selected_task)
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file_name_to_uris = {split_uri[-1]: "/".join(split_uri) for split_uri in split_artifact_uris}
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if selected_task_recording_uri:
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streamlit_show_recording(tab_recording, selected_task_recording_uri)
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for file_name, uri in file_name_to_uris.items():
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if task_steps:
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file_name = file_name.lower()
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col_steps_prev, _, col_steps_next = col_steps.columns([3, 1, 3])
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if file_name.endswith("screenshot_llm.png") or file_name.endswith("screenshot.png"):
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col_steps_prev.button(
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streamlit_content_safe(
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"prev", on_click=go_to_previous_step, key="previous_step_button", use_container_width=True
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tab_screenshot,
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)
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tab_screenshot.image,
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col_steps_next.button(
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read_artifact_safe(uri, is_image=True),
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"next", on_click=go_to_next_step, key="next_step_button", use_container_width=True
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"No screenshot available.",
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)
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use_column_width=True,
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)
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step_id_buttons = {
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elif file_name.endswith("screenshot_action.png"):
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step["step_id"]: col_steps.button(
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streamlit_content_safe(
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f"{step['order']} - {step['retry_index']} - {step['step_id']}",
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tab_post_action_screenshot,
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on_click=select_step,
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tab_post_action_screenshot.image,
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args=(step,),
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read_artifact_safe(uri, is_image=True),
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use_container_width=True,
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"No action screenshot available.",
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type="primary" if selected_step and step["step_id"] == selected_step["step_id"] else "secondary",
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use_column_width=True,
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)
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)
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for step in task_steps
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elif file_name.endswith("id_xpath_map.json"):
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}
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streamlit_content_safe(
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tab_id_to_xpath, tab_id_to_xpath.json, read_artifact_safe(uri), "No ID -> XPath map available."
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df = pd.json_normalize(task_steps)
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)
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tab_task_steps.dataframe(df, use_container_width=True, height=1000)
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elif file_name.endswith("tree.json"):
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streamlit_content_safe(
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task_action_results = []
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tab_element_tree,
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for step in task_steps:
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tab_element_tree.json,
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output = step.get("output")
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read_artifact_safe(uri),
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step_id = step["step_id"]
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"No element tree available.",
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if output:
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)
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step_action_results = output.get("action_results", [])
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elif file_name.endswith("tree_trimmed.json"):
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for action_result in step_action_results:
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streamlit_content_safe(
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task_action_results.append(
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tab_element_tree_trimmed,
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{
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tab_element_tree_trimmed.json,
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"step_id": step_id,
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read_artifact_safe(uri),
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"order": step["order"],
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"No element tree trimmed available.",
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"retry_index": step["retry_index"],
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)
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**action_result,
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elif file_name.endswith("llm_prompt.txt"):
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}
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content = read_artifact_safe(uri)
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)
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# this is a hacky way to call this generic method to get it working with st.text_area
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df = pd.json_normalize(task_action_results)
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streamlit_content_safe(
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df = df.reindex(sorted(df.columns), axis=1)
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tab_llm_prompt,
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tab_task_action_results.dataframe(df, use_container_width=True, height=1000)
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tab_llm_prompt.text_area,
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content,
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if selected_step:
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"No LLM prompt available.",
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tab_step.json(selected_step)
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value=content,
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height=1000,
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artifacts_response = repository.get_artifacts(selected_task["task_id"], selected_step["step_id"])
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label_visibility="collapsed",
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split_artifact_uris = [artifact["uri"].split("/") for artifact in artifacts_response]
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)
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file_name_to_uris = {split_uri[-1]: "/".join(split_uri) for split_uri in split_artifact_uris}
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# tab_llm_prompt.text_area("collapsed", value=content, label_visibility="collapsed", height=1000)
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elif file_name.endswith("llm_request.json"):
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for file_name, uri in file_name_to_uris.items():
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streamlit_content_safe(
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file_name = file_name.lower()
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tab_llm_request, tab_llm_request.json, read_artifact_safe(uri), "No LLM request available."
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if file_name.endswith("screenshot_llm.png") or file_name.endswith("screenshot.png"):
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)
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streamlit_content_safe(
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elif file_name.endswith("llm_response_parsed.json"):
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tab_screenshot,
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streamlit_content_safe(
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tab_screenshot.image,
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tab_llm_response_parsed,
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read_artifact_safe(uri, is_image=True),
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tab_llm_response_parsed.json,
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"No screenshot available.",
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read_artifact_safe(uri),
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use_column_width=True,
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"No parsed LLM response available.",
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)
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)
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elif file_name.endswith("screenshot_action.png"):
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elif file_name.endswith("llm_response.json"):
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streamlit_content_safe(
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streamlit_content_safe(
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tab_post_action_screenshot,
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tab_llm_response_raw,
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tab_post_action_screenshot.image,
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tab_llm_response_raw.json,
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read_artifact_safe(uri, is_image=True),
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read_artifact_safe(uri),
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"No action screenshot available.",
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"No raw LLM response available.",
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use_column_width=True,
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)
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)
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elif file_name.endswith("html_scrape.html"):
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elif file_name.endswith("id_xpath_map.json"):
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streamlit_content_safe(tab_html, tab_html.text, read_artifact_safe(uri), "No html available.")
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streamlit_content_safe(
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elif file_name.endswith("html_action.html"):
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tab_id_to_xpath,
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streamlit_content_safe(tab_html, tab_html.text, read_artifact_safe(uri), "No html available.")
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tab_id_to_xpath.json,
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else:
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read_artifact_safe(uri),
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st.write(f"Artifact {file_name} not supported.")
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"No ID -> XPath map available.",
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)
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elif file_name.endswith("tree.json"):
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streamlit_content_safe(
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tab_element_tree,
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tab_element_tree.json,
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read_artifact_safe(uri),
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"No element tree available.",
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)
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elif file_name.endswith("tree_trimmed.json"):
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streamlit_content_safe(
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tab_element_tree_trimmed,
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tab_element_tree_trimmed.json,
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read_artifact_safe(uri),
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"No element tree trimmed available.",
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)
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elif file_name.endswith("llm_prompt.txt"):
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content = read_artifact_safe(uri)
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# this is a hacky way to call this generic method to get it working with st.text_area
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streamlit_content_safe(
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tab_llm_prompt,
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tab_llm_prompt.text_area,
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content,
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"No LLM prompt available.",
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value=content,
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height=1000,
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label_visibility="collapsed",
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)
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# tab_llm_prompt.text_area("collapsed", value=content, label_visibility="collapsed", height=1000)
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elif file_name.endswith("llm_request.json"):
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streamlit_content_safe(
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tab_llm_request, tab_llm_request.json, read_artifact_safe(uri), "No LLM request available."
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)
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elif file_name.endswith("llm_response_parsed.json"):
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streamlit_content_safe(
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tab_llm_response_parsed,
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tab_llm_response_parsed.json,
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read_artifact_safe(uri),
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"No parsed LLM response available.",
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)
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elif file_name.endswith("llm_response.json"):
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streamlit_content_safe(
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tab_llm_response_raw,
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tab_llm_response_raw.json,
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read_artifact_safe(uri),
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"No raw LLM response available.",
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)
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elif file_name.endswith("html_scrape.html"):
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streamlit_content_safe(tab_html, tab_html.text, read_artifact_safe(uri), "No html available.")
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elif file_name.endswith("html_action.html"):
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streamlit_content_safe(tab_html, tab_html.text, read_artifact_safe(uri), "No html available.")
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else:
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st.write(f"Artifact {file_name} not supported.")
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