engineering/opencompletion.com
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Use selected model for activity AI operations

Browse source 
Pass the selected model parameter through the entire activity workflow
to ensure all AI operations (categorization, translation, feedback
generation, and grading) use the user's chosen model instead of
defaulting to the system default. Falls back to default when no model
is selected.
This commit is contained in:
Russell Ballestrini 2025-10-22 20:04:20 -04:00
parent f0c7ea2cf5
commit 77e2c04ec0
1 changed files with 60 additions and 27 deletions
Download patch file Download diff file Expand all files Collapse all files

87
app.py
View file

@ -638,7 +638,7 @@ def handle_message(data):
activity_state = ActivityState.query.filter_by(room_id=room.id).first()
if activity_state:
gevent.spawn(handle_activity_response, room_name, message, username)
gevent.spawn(handle_activity_response, room_name, message, username, model)
return
if model != "None":
@ -1569,7 +1569,7 @@ def get_activity_content(file_path):
def loop_through_steps_until_question(
activity_content, activity_state, room_name, username
activity_content, activity_state, room_name, username, model=None
):
room = get_room(room_name)
@ -1600,7 +1600,7 @@ def loop_through_steps_until_question(
# Emit the current step content blocks
if "content_blocks" in step:
content = "\n\n".join(step["content_blocks"])
translated_content = translate_text(content, user_language)
translated_content = translate_text(content, user_language, model)
new_message = Message(
username="System", content=translated_content, room_id=room.id
)
@ -1622,7 +1622,7 @@ def loop_through_steps_until_question(
if "question" in step:
question_content = step["question"]
translated_question_content = translate_text(
question_content, user_language
question_content, user_language, model
)
new_message = Message(
username="System (Question)",
@ -1663,7 +1663,7 @@ def loop_through_steps_until_question(
# Activity completed
# Display activity info before completing
display_activity_info(room_name, username)
display_activity_info(room_name, username, model)
db.session.delete(activity_state)
db.session.commit()
@ -1702,7 +1702,7 @@ def start_activity(room_name, s3_file_path, username):
# Loop through steps until a question is found or the end is reached
loop_through_steps_until_question(
activity_content, activity_state, room_name, username
activity_content, activity_state, room_name, username, model
)
# Emit activity status update
@ -1807,7 +1807,7 @@ def execute_processing_script(metadata, script):
return local_env["script_result"]
def handle_activity_response(room_name, user_response, username):
def handle_activity_response(room_name, user_response, username, model=None):
with app.app_context():
room = get_room(room_name)
activity_state = ActivityState.query.filter_by(room_id=room.id).first()
@ -1854,6 +1854,7 @@ def handle_activity_response(room_name, user_response, username):
user_response,
step["buckets"],
step.get("tokens_for_ai", ""),
model,
)
# Initialize transition to None
@ -2161,7 +2162,7 @@ def handle_activity_response(room_name, user_response, username):
if "content_blocks" in transition:
transition_content = "\n\n".join(transition["content_blocks"])
translated_transition_content = translate_text(
transition_content, user_language
transition_content, user_language, model
)
new_message = Message(
username="System",
@ -2201,6 +2202,7 @@ def handle_activity_response(room_name, user_response, username):
json.dumps(activity_state.dict_metadata), # Pass full metadata
json.dumps(new_metadata),
feedback_tokens_for_ai, # Pass legacy tokens to be combined
model,
)
feedback_messages.extend(multi_feedback_messages)
elif feedback_tokens_for_ai:
@ -2224,6 +2226,7 @@ def handle_activity_response(room_name, user_response, username):
username,
json.dumps(feedback_metadata),
json.dumps(new_metadata),
model,
)
if feedback and feedback.strip():
feedback_messages.append(
@ -2310,7 +2313,7 @@ def handle_activity_response(room_name, user_response, username):
# Loop through steps until a question is found or the end is reached
loop_through_steps_until_question(
activity_content, activity_state, room_name, username
activity_content, activity_state, room_name, username, model
)
else:
# the user response is any bucket other than correct.
@ -2322,7 +2325,7 @@ def handle_activity_response(room_name, user_response, username):
# Emit the question again
question_content = step["question"]
translated_question_content = translate_text(
question_content, user_language
question_content, user_language, model
)
new_message = Message(
username="System (Question)",
@ -2361,7 +2364,7 @@ def handle_activity_response(room_name, user_response, username):
else:
# Handle steps without a question
loop_through_steps_until_question(
activity_content, activity_state, room_name, username
activity_content, activity_state, room_name, username, model
)
except Exception as e:
@ -2379,7 +2382,7 @@ def handle_activity_response(room_name, user_response, username):
)
def display_activity_info(room_name, username):
def display_activity_info(room_name, username, model=None):
with app.app_context():
room = get_room(room_name)
activity_state = ActivityState.query.filter_by(room_id=room.id).first()
@ -2433,7 +2436,7 @@ def display_activity_info(room_name, username):
)
# Generate the grading using the AI
grading_message = generate_grading(chat_history, rubric)
grading_message = generate_grading(chat_history, rubric, model)
# Store and emit the activity info
info_message = f"Activity Info:\nCurrent Section: {activity_state.section_id}\nCurrent Step: {activity_state.step_id}\nAttempts: {activity_state.attempts}\n\n{grading_message}"
@ -2467,8 +2470,12 @@ def display_activity_info(room_name, username):
print(f"Exception: {e}")
def generate_grading(chat_history, rubric):
openai_client, model_name = get_openai_client_and_model()
def generate_grading(chat_history, rubric, model=None):
# Use provided model or fall back to default
if model and model != "None":
openai_client, model_name = get_openai_client_and_model(model)
else:
openai_client, model_name = get_openai_client_and_model()
messages = [
{
"role": "system",
@ -2515,8 +2522,12 @@ def get_next_step(activity_content, current_section_id, current_step_id):
# Categorize the user's response.
def categorize_response(question, response, buckets, tokens_for_ai):
openai_client, model_name = get_openai_client_and_model()
def categorize_response(question, response, buckets, tokens_for_ai, model=None):
# Use provided model or fall back to default
if model and model != "None":
openai_client, model_name = get_openai_client_and_model(model)
else:
openai_client, model_name = get_openai_client_and_model()
bucket_list = ", ".join([str(bucket) for bucket in buckets])
# Check if tokens_for_ai already includes format instructions (ANALYSIS/BUCKET format)
if "ANALYSIS:" in tokens_for_ai and "BUCKET:" in tokens_for_ai:
@ -2592,8 +2603,13 @@ def generate_ai_feedback(
username,
json_metadata,
json_new_metadata,
model=None,
):
openai_client, model_name = get_openai_client_and_model()
# Use provided model or fall back to default
if model and model != "None":
openai_client, model_name = get_openai_client_and_model(model)
else:
openai_client, model_name = get_openai_client_and_model()
messages = [
{
"role": "system",
@ -2625,6 +2641,7 @@ def provide_feedback(
username,
json_metadata,
json_new_metadata,
model=None,
):
feedback = ""
if "ai_feedback" in transition:
@ -2637,6 +2654,7 @@ def provide_feedback(
username,
json_metadata,
json_new_metadata,
model,
)
feedback += f"\n\n{ai_feedback}"
@ -2654,6 +2672,7 @@ def provide_feedback_prompts(
json_metadata,
json_new_metadata,
legacy_tokens_for_ai="",
model=None,
):
"""Generate feedback from multiple prompts"""
feedback_messages = []
@ -2675,22 +2694,31 @@ def provide_feedback_prompts(
prompt_metadata = {
k: v for k, v in full_metadata.items() if k in filter_keys
}
# Check skip condition if specified
skip_condition = prompt.get("skip_condition")
if skip_condition:
should_skip = False
values = list(prompt_metadata.values())
if skip_condition == "all_null":
should_skip = all(value is None or value == "" or value == "None" for value in values)
should_skip = all(
value is None or value == "" or value == "None"
for value in values
)
elif skip_condition == "all_false":
should_skip = all(value is False or value == "False" for value in values)
should_skip = all(
value is False or value == "False" for value in values
)
elif skip_condition == "all_true":
should_skip = all(value is True or value == "True" for value in values)
should_skip = all(
value is True or value == "True" for value in values
)
if should_skip:
print(f"DEBUG: Skipping prompt '{prompt_name}' - skip_condition '{skip_condition}' met")
print(
f"DEBUG: Skipping prompt '{prompt_name}' - skip_condition '{skip_condition}' met"
)
continue
# Special debug for Ship Status and Game Over
@ -2752,6 +2780,7 @@ def provide_feedback_prompts(
username,
json.dumps(prompt_metadata), # Use filtered metadata for this prompt
json_new_metadata,
model,
)
# Only add feedback if it has content
@ -2763,14 +2792,18 @@ def provide_feedback_prompts(
return feedback_messages
def translate_text(text, target_language):
def translate_text(text, target_language, model=None):
# Guard clause for default language
target_language = target_language.lower().split()
if "english" in target_language:
return text
openai_client, model_name = get_openai_client_and_model()
# Use provided model or fall back to default
if model and model != "None":
openai_client, model_name = get_openai_client_and_model(model)
else:
openai_client, model_name = get_openai_client_and_model()
messages = [
{
"role": "system",