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Update app.py
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import os
from collections import defaultdict
from database import save_response, read_responses
import gradio as gr
import pandas as pd
import random
css = """
.rtl
{
text-align: right;
}
.usr-inst{
text-align:center;
border: solid 1px;
border-radius: 5px;
padding: 10px;
}
.svelte-1kzox3m{
justify-content: end;
}
.svelte-sfqy0y{
border:none;
}
.svelte-90oupt{
padding-top: 0px;
}
#component-4{
border: 1px solid;
padding: 5px;
border-radius: 5px;
}
"""
file_path = 'instructions/merged.json'
df = pd.read_json(file_path, orient='records', lines=False)
# that keeps track of how many times each question has been used
question_count = {index: 0 for index in df.index}
model_rankings = defaultdict(lambda: {'1st': 0, '2nd': 0, '3rd': 0})
curr_order = ['CIDAR', 'CHAT', 'ALPAGASUS']
def get_rank_suffix(rank):
if 11 <= rank <= 13:
return 'th'
else:
suffixes = {1: 'st', 2: 'nd', 3: 'rd'}
return suffixes.get(rank % 10, 'th')
def process_rankings(user_rankings):
print("Processing Rankings:", user_rankings) # Debugging print
save_response(user_rankings)
print(read_responses())
return
def get_questions_and_answers():
available_questions = [index for index, count in question_count.items() if count < 3]
index = random.sample(available_questions, min(1, len(available_questions)))[0]
question_count[index] += 1
question = df.loc[index, 'instruction']
answers_with_models = [
(df.loc[index, 'cidar_output'], 'CIDAR'),
(df.loc[index, 'chat_output'], 'CHAT'),
(df.loc[index, 'alpagasus_output'], 'ALPAGASUS')
]
random.shuffle(answers_with_models) # Shuffle answers with their IDs
curr_order = [model for _, model in answers_with_models]
return (question, answers_with_models)
def reload_components():
question, answers = get_questions_and_answers()
user_instructions_txt = " في الصفحة التالية ستجد طلب له ثلاث إجابات مختلفة. من فضلك اختر مدي توافق كل إجابة مع الثقافة العربية."
radios = []
user_instructions = gr.Markdown(rtl=True, value= f'<h1 class="usr-inst">{user_instructions_txt}</h1>')
question_md = gr.Markdown(rtl=True, value= f'<b> {question} </b>')
for answer, model in answers:
radios.append(gr.Markdown(rtl = True, value= answer))
radios.append(gr.Radio(elem_classes = 'rtl', choices = ['متوافق', 'متوافق جزئياً', 'غير متوافق'], value = 'غير متوافق', label = ""))
return [user_instructions, question_md] + radios
def rank_interface():
def rank_fluency(*radio_selections):
user_rankings = {}
for i in range(0, len(radio_selections), 3): # Process each set of 3 dropdowns for a question
selections = radio_selections[i:i+3]
for j, chosen_answer in enumerate(selections):
model_name = curr_order[j]
if chosen_answer == 'غير متوافق':
user_rankings[model_name] = 3
elif chosen_answer == 'متوافق جزئياً':
user_rankings[model_name] = 2
elif chosen_answer == 'متوافق':
user_rankings[model_name] = 1
process_rankings(user_rankings)
return "سجلنا ردك، ما قصرت =)"
# Create three dropdowns for each question for 1st, 2nd, and 3rd choices
inputs = []
with gr.Blocks(css=css) as demo:
with gr.Row():
with gr.Column():
outptus= reload_components()
out_text = gr.Markdown("", rtl = True)
gr.Button("Submit").click(
fn=rank_fluency,
inputs=outptus[1:],
outputs=out_text
).then(
fn=reload_components,
outputs = outptus
)
gr.Button("Skip").click(
fn=reload_components,
outputs=outptus
)
return demo
questions = get_questions_and_answers()
iface = rank_interface()
iface.launch(share = True)