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Added exception handling, structure
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@dschiese
dschiese committed Jun 14, 2024
1 parent d37049f commit 319198a
Showing 1 changed file with 71 additions and 95 deletions.
166 changes: 71 additions & 95 deletions explanation_frontend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,12 +125,15 @@
# Fetches the available components from the associated Qanary pipeline
@st.cache_data
def request_components_list():
response = requests.get(QANARY_PIPELINE_COMPONENTS, headers={"Accept":"application/json"}) # Auslagern der URL
data = json.loads(response.text)
components = []
for key in data:
components.append(key["name"])
return components
try:
response = requests.get(QANARY_PIPELINE_COMPONENTS, headers={"Accept":"application/json"}) # Auslagern der URL
data = json.loads(response.text)
components = []
for key in data:
components.append(key["name"])
return components
except Exception as e:
raise Exception("Error while fetching the components: " + str(e))

# Executes the Qanary pipeline with the passed components, the gptModel attr is passed to check whether it must be executed or can be taken from the cache
@st.cache_data
Expand All @@ -139,23 +142,31 @@ def execute_qanary_pipeline(question, components, gptModel):
for component in components:
component_list += "&componentlist[]=" + component
custom_pipeline_url = f"{QANARY_PIPELINE_URL}/questionanswering?textquestion=" + question + component_list
return requests.post(custom_pipeline_url, {})
try:
response = requests.post(custom_pipeline_url, {})
return response
except Exception as e:
return e

# Fetches the explanations for the input data
@st.cache_data
def input_data_explanation(json):
input_explanation_url = f"{QANARY_EXPLANATION_SERVICE_URL}/composedexplanations/inputdata"
response = requests.post(input_explanation_url, json, headers={"Accept":"application/json","Content-Type":"application/json"})
print("Response: " + response.text)
return response.text
if(response.status_code != 200):
raise Exception("Error while fetching the input data explanations: " + response.text)
elif(200 <= response.status_code < 300):
return response.text

# Fetches the explanations for the output data
@st.cache_data
def output_data_explanation(json):
output_explanation_url = f"{QANARY_EXPLANATION_SERVICE_URL}/composedexplanations/outputdata"
response = requests.post(output_explanation_url, json, headers={"Accept":"application/json","Content-Type":"application/json"})
print("Response: " + response.text)
return response.text
if(response.status_code != 200):
raise Exception("Error while fetching the output data explanations: " + response.text)
elif(200 <= response.status_code < 300):
return response.text

# Helper function to convert the dict to a array of components # TODO: Needed!?
def convert_component_dir_to_list(componentDir):
Expand Down Expand Up @@ -194,71 +205,45 @@ def request_explanations(question, gptModel):
st.session_state.explanations_generated = False
st.session_state.process_active = True
components = convert_component_dir_to_list(st.session_state.selected_configuration["components"])
qa_process_information = execute_qanary_pipeline(question, components, gptModel).json()
st.session_state.pipeline_finished = True
graph = qa_process_information["outGraph"]
json_data = json.dumps({
"graphUri": graph,
"generativeExplanationRequest": {
"shots": gptModels_dic[gptModel][SHOTS_KEY], #Rename gpt models dict as it contains the shots value
"gptModel": gptModels_dic[gptModel][MODEL_KEY],
"qanaryComponents": components
}})

input_data_explanations = json.loads(input_data_explanation(json_data))
output_data_explanations = json.loads(output_data_explanation(json_data))

currentQaProcessExplanations = {
"components": {},
"meta_information": {
"graphUri": graph,
"questionUri": qa_process_information["question"]
}
}

for component in components:
input = input_data_explanations["explanationItems"][component]
output = output_data_explanations["explanationItems"][component]
currentQaProcessExplanations["components"][component] = createExplanationDict(input, output)
try:
qa_process_information = execute_qanary_pipeline(question, components, gptModel).json()
st.session_state.pipeline_finished = True
graph = qa_process_information["outGraph"]
json_data = json.dumps({
"graphUri": graph,
"generativeExplanationRequest": {
"shots": gptModels_dic[gptModel][SHOTS_KEY], #Rename gpt models dict as it contains the shots value
"gptModel": gptModels_dic[gptModel][MODEL_KEY],
"qanaryComponents": components
}})

input_data_explanations = json.loads(input_data_explanation(json_data))
output_data_explanations = json.loads(output_data_explanation(json_data))

currentQaProcessExplanations = {
"components": {},
"meta_information": {
"graphUri": graph,
"questionUri": qa_process_information["question"]
}
}

st.session_state.currentQaProcessExplanations = currentQaProcessExplanations
st.session_state.componentsSelection = currentQaProcessExplanations["components"].keys()
st.session_state.explanations_generated = True
for component in components:
input = input_data_explanations["explanationItems"][component]
output = output_data_explanations["explanationItems"][component]
currentQaProcessExplanations["components"][component] = createExplanationDict(input, output)

st.session_state.currentQaProcessExplanations = currentQaProcessExplanations
st.session_state.componentsSelection = currentQaProcessExplanations["components"].keys()
st.session_state.explanations_generated = True
except Exception as e:
logging.error("Error while executing the Qanary pipeline: " + str(e))
st.error("Error while executing the Qanary pipeline. Try another configuration or try again later.")
st.session_state.pipeline_finished = False
st.cache_data.clear()

##### definitions for configurations

def showExplanationContainer2(component, lang, plainKey, datasetTitle): # use the current component instead
generative = (component["generative"]).strip("\n")
template = (component["rulebased"]).strip("\n")
with st.container(border=False):
col1, col2 = st.columns([0.4,0.6])
with col1:
template_based_container = st.container()
with template_based_container:
st.markdown(f"""<h3>Template</h3>""", unsafe_allow_html=True)
text_template, buttons_template = st.columns([0.85,0.15])
with text_template:
st.markdown(f"""<div style="margin-bottom: 25px;">{template}</div>""", unsafe_allow_html=True)
with buttons_template:
feedback_button(plainKey+"template"+"correct",":white_check_mark:")
feedback_button(plainKey+"template"+"wrong",":x:")
generative_based_container = st.container()
with generative_based_container:
st.markdown(f"""<h3>Generative</h3>""", unsafe_allow_html=True)
text_template, buttons_template = st.columns([0.85,0.15])
with text_template:
st.markdown(f"""<div style="margin-bottom: 25px;">{generative}</div>""", unsafe_allow_html=True)
with buttons_template:
feedback_button(plainKey+"generative"+"correct",":white_check_mark:")
feedback_button(plainKey+"generative"+"wrong",":x:")
with col2:
st.markdown("""<h3>{datasetTitle}</h3>""", unsafe_allow_html=True)
code_editor(component["dataset"],lang="text", theme="default", options={"wrap": True})
st.divider()
with st.expander("Show used prompt"):
code_editor(component["prompt"], lang="text", theme="default", options={"wrap": True})

def showExplanationContainer(component, lang, plainKey, datasetTitle):
generative = (component["generative"]).strip("\n")
template = (component["rulebased"]).strip("\n")
Expand Down Expand Up @@ -303,6 +288,7 @@ def send_feedback(explanation, explanation_type, feedback):
})
except Exception as e:
logging.error("Feedback wasn't sent: " + str(e))
st.error("Feedback wasn't sent. Sorry for the circumstances.")

def show_meta_data():
if st.session_state.pipeline_finished:
Expand All @@ -326,6 +312,14 @@ def show_explanations():
else:
st.write("You haven't selected a configuration or individual components")

def exampleQuestion(key, question):
button, text = st.columns([0.05,0.95])
with button:
if st.button(key=key,label=":white_check_mark:"):
st.session_state.text_question = question
with text:
st.write(question)

##### Configured
def pre_configured():
if st.session_state.pipeline_finished:
Expand All @@ -335,7 +329,6 @@ def pre_configured():
show_explanations()

##### Not configured

def not_pre_configured():
components = request_components_list()
componentsNames = convert_component_dir_to_list(components)
Expand All @@ -345,18 +338,12 @@ def not_pre_configured():

st.session_state.selected_configuration["components"] = st.multiselect(label="Select your components in the correct order", label_visibility="hidden",options=componentsNames, key="compSelectionIndividual", placeholder="Choose your desired components")

if st.session_state.pipeline_finished:
st.divider()
show_meta_data()
st.divider()
if st.session_state.explanations_generated:
show_explanations()
pre_configured()

### Start streamlit app
### START STREAMLIT APP

st.header('Qanary Explanation Demo')


with st.sidebar:
if st.session_state.showPreconfigured:
st.subheader("Default configurations", help="Select a pre-defined configuration to start the Qanary pipeline with.")
Expand All @@ -369,22 +356,10 @@ def not_pre_configured():
if not st.session_state.showPreconfigured:
configButton = st.button("Change configuration", on_click=lambda: switch_view())

def exampleQuestion(key, question):
button, text = st.columns([0.05,0.95])
with button:
if st.button(key=key,label=":white_check_mark:"):
st.session_state.text_question = question
with text:
st.write(question)


### Header

header_column, button_column = st.columns(2)

with header_column:
st.subheader("Enter a question")
# st.write(f'<span style="font-size: 1.1rem;">{st.session_state.selected_configuration["exampleQuestions"]}</span>', unsafe_allow_html=True)

question, submit_question = st.columns([5, 1])

Expand All @@ -393,9 +368,10 @@ def exampleQuestion(key, question):
with submit_question:
st.button('Send', on_click=lambda: request_explanations(text_question, gptModel))

with st.expander("Example questions"):
for question in st.session_state.selected_configuration["exampleQuestions"]:
exampleQuestion(question, question)
if st.session_state.showPreconfigured:
with st.expander("Example questions"):
for question in st.session_state.selected_configuration["exampleQuestions"]:
exampleQuestion(question, question)

text_question = placeholder.text_input(key="text_question", label='Your question', value="When was Albert Einstein born?", label_visibility="collapsed")

Expand Down

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