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import gradio as gr
import json
# Import your modules here
from Agents.togetherAIAgent import generate_article_from_query
from Agents.wikiAgent import get_wiki_data
from Agents.rankerAgent import rankerAgent
from Query_Modification.QueryModification import query_Modifier, getKeywords
from Ranking.RRF.RRF_implementation import reciprocal_rank_fusion_three, reciprocal_rank_fusion_six
from Retrieval.tf_idf import tf_idf_pipeline
from Retrieval.bm25 import bm25_pipeline
from Retrieval.vision import vision_pipeline
from Retrieval.openSource import open_source_pipeline
from Baseline.boolean import boolean_pipeline
from AnswerGeneration.getAnswer import generate_answer_withContext, generate_answer_zeroShot
# Load miniWikiCollection
miniWikiCollection = json.load(open('Datasets/mini_wiki_collection.json', 'r'))
miniWikiCollectionDict = {wiki['wikipedia_id']: " ".join(wiki['text']) for wiki in miniWikiCollection}
def process_query(query):
# Query modification
modified_query = query_Modifier(query)
# Context Generation
article = generate_article_from_query(query)
# Keyword Extraction and getting context from Wiki
keywords = getKeywords(query)
wiki_data = get_wiki_data(keywords)
# Retrieve rankings
boolean_ranking = boolean_pipeline(query)
tf_idf_ranking = tf_idf_pipeline(query)
bm25_ranking = bm25_pipeline(query)
vision_ranking = vision_pipeline(query)
open_source_ranking = open_source_pipeline(query)
# Modified queries
boolean_ranking_modified = boolean_pipeline(modified_query)
tf_idf_ranking_modified = tf_idf_pipeline(modified_query)
bm25_ranking_modified = bm25_pipeline(modified_query)
vision_ranking_modified = vision_pipeline(modified_query)
open_source_ranking_modified = open_source_pipeline(modified_query)
# RRF rankings
tf_idf_bm25_open_RRF_Ranking = reciprocal_rank_fusion_three(tf_idf_ranking, bm25_ranking, open_source_ranking)
tf_idf_bm25_open_RRF_Ranking_modified = reciprocal_rank_fusion_three(tf_idf_ranking_modified, bm25_ranking_modified, open_source_ranking_modified)
tf_idf_bm25_open_RRF_Ranking_combined = reciprocal_rank_fusion_six(
tf_idf_ranking, bm25_ranking, open_source_ranking,
tf_idf_ranking_modified, bm25_ranking_modified, open_source_ranking_modified
)
try:
agent1_context = wiki_data[0]
except:
agent1_context = "Can't find a Wiki article for this query."
agent2_context = article
try:
boolean_context = miniWikiCollectionDict[boolean_ranking[0]]
except:
boolean_context = "Can't find a matching document for this query."
tf_idf_context = miniWikiCollectionDict[tf_idf_ranking[0]]
bm25_context = miniWikiCollectionDict[str(bm25_ranking[0])]
vision_context = miniWikiCollectionDict[vision_ranking[0]]
open_source_context = miniWikiCollectionDict[open_source_ranking[0]]
boolean_context_modified = miniWikiCollectionDict[boolean_ranking_modified[0]]
tf_idf_context_modified = miniWikiCollectionDict[tf_idf_ranking_modified[0]]
bm25_context_modified = miniWikiCollectionDict[str(bm25_ranking_modified[0])]
vision_context_modified = miniWikiCollectionDict[vision_ranking_modified[0]]
open_source_context_modified = miniWikiCollectionDict[open_source_ranking_modified[0]]
tf_idf_bm25_open_RRF_Ranking_context = miniWikiCollectionDict[tf_idf_bm25_open_RRF_Ranking[0]]
tf_idf_bm25_open_RRF_Ranking_modified_context = miniWikiCollectionDict[tf_idf_bm25_open_RRF_Ranking_modified[0]]
tf_idf_bm25_open_RRF_Ranking_combined_context = miniWikiCollectionDict[tf_idf_bm25_open_RRF_Ranking_combined[0]]
# Generating answers
agent1_answer = generate_answer_withContext(query, agent1_context)
agent2_answer = generate_answer_withContext(query, agent2_context)
boolean_answer = generate_answer_withContext(query, boolean_context)
tf_idf_answer = generate_answer_withContext(query, tf_idf_context)
bm25_answer = generate_answer_withContext(query, bm25_context)
vision_answer = generate_answer_withContext(query, vision_context)
open_source_answer = generate_answer_withContext(query, open_source_context)
boolean_answer_modified = generate_answer_withContext(modified_query, boolean_context_modified)
tf_idf_answer_modified = generate_answer_withContext(modified_query, tf_idf_context_modified)
bm25_answer_modified = generate_answer_withContext(modified_query, bm25_context_modified)
vision_answer_modified = generate_answer_withContext(modified_query, vision_context_modified)
open_source_answer_modified = generate_answer_withContext(modified_query, open_source_context_modified)
tf_idf_bm25_open_RRF_Ranking_answer = generate_answer_withContext(query, tf_idf_bm25_open_RRF_Ranking_context)
tf_idf_bm25_open_RRF_Ranking_modified_answer = generate_answer_withContext(modified_query, tf_idf_bm25_open_RRF_Ranking_modified_context)
tf_idf_bm25_open_RRF_Ranking_combined_answer = generate_answer_withContext(query, tf_idf_bm25_open_RRF_Ranking_combined_context)
zeroShot = generate_answer_zeroShot(query)
# Ranking the best answer
rankerAgentInput = {
"query": query,
"agent1": agent1_answer,
"agent2": agent2_answer,
"boolean": boolean_answer,
"tf_idf": tf_idf_answer,
"bm25": bm25_answer,
"vision": vision_answer,
"open_source": open_source_answer,
"boolean_modified": boolean_answer_modified,
"tf_idf_modified": tf_idf_answer_modified,
"bm25_modified": bm25_answer_modified,
"vision_modified": vision_answer_modified,
"open_source_modified": open_source_answer_modified,
"tf_idf_bm25_open_RRF_Ranking": tf_idf_bm25_open_RRF_Ranking_answer,
"tf_idf_bm25_open_RRF_Ranking_modified": tf_idf_bm25_open_RRF_Ranking_modified_answer,
"tf_idf_bm25_open_RRF_Ranking_combined": tf_idf_bm25_open_RRF_Ranking_combined_answer,
"zeroShot": zeroShot
}
best_model, best_answer = rankerAgent(rankerAgentInput)
return (
best_model,
best_answer,
agent1_answer, agent1_context,
agent2_answer, agent2_context,
boolean_answer, boolean_context,
tf_idf_answer, tf_idf_context,
bm25_answer, bm25_context,
vision_answer, vision_context,
open_source_answer, open_source_context,
boolean_answer_modified, boolean_context_modified,
tf_idf_answer_modified, tf_idf_context_modified,
bm25_answer_modified, bm25_context_modified,
vision_answer_modified, vision_context_modified,
open_source_answer_modified, open_source_context_modified,
tf_idf_bm25_open_RRF_Ranking_answer, tf_idf_bm25_open_RRF_Ranking_context,
tf_idf_bm25_open_RRF_Ranking_modified_answer, tf_idf_bm25_open_RRF_Ranking_modified_context,
tf_idf_bm25_open_RRF_Ranking_combined_answer, tf_idf_bm25_open_RRF_Ranking_combined_context,
zeroShot, "Zero-shot doesn't have a context."
)
# CSS Styling for the fancy effects
css = """
#fancy-column {
background: linear-gradient(135deg, #1a242f, #2b3a44); /* Dark blue-gray gradient background */
padding: 20px;
border-radius: 15px;
}
#query-input, #submit-button, #best-model-output, #best-answer-output {
border-radius: 10px; /* Rounded corners */
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3); /* Darker shadow for better contrast */
background-color: #34495e; /* Dark background for inputs */
color: #ecf0f1; /* Light text for good readability */
}
#query-input:focus, #submit-button:focus, #best-model-output:focus, #best-answer-output:focus {
outline: none;
border: 2px solid #7f8c8d; /* Subtle accent border on focus */
}
#submit-button {
background-color: #16a085; /* Muted teal color for button */
color: #ecf0f1; /* Light text for button */
font-weight: bold;
padding: 10px;
}
#submit-button:hover {
background-color: #1abc9c; /* Slightly lighter teal on hover */
}
#best-model-output, #best-answer-output {
background-color: #2c3e50; /* Darker background for output boxes */
}
#best-model-output label, #best-answer-output label, #query-input label {
color: #ecf0f1; /* Light text for labels */
}
"""
# Interface creation
def create_interface():
with gr.Blocks() as interface:
with gr.Column(elem_id="fancy-column", scale=3): # Fancy column with extra styling
with gr.Row():
query_input = gr.Textbox(label="Enter your query", scale=3, elem_id="query-input")
submit_button = gr.Button("Submit", scale=1, elem_id="submit-button")
# Adjusting the spacing between the output fields
with gr.Row():
best_model_output = gr.Textbox(label="Best Model", interactive=False, scale=1.5, elem_id="best-model-output")
best_answer_output = gr.Textbox(label="Best Answer", interactive=False, scale=1.5, elem_id="best-answer-output")
with gr.Column():
# Function to create a row for answers and contexts
def create_answer_row(label):
if label == "Agent 1":
label = "Wiki Search"
elif label == "Agent 2":
label = "Llama Context Generation"
elif label == "Open Source Answer":
label = 'MiniLM Text Embedding model'
elif label == "Open Source (Modified)":
label = 'MiniLM Text Embedding model (Modified)'
elif label == "TF-IDF + BM25 + Open RRF":
label = "RRF (TF-IDF + BM25 + MiniLM)"
elif label == "TF-IDF + BM25 + Open RRF (Modified)":
label = "RRF (TF-IDF + BM25 + MiniLM) (Modified)"
elif label == "TF-IDF + BM25 + Open RRF (Combined)":
label = "RRF (TF-IDF + BM25 + MiniLM) (Combined)"
with gr.Row():
answer_textbox = gr.Textbox(label=f"{label} Answer", interactive=False, scale=1.2, elem_id="best-model-output")
context_textbox = gr.Textbox(label=f"{label} Context", scale=1.8, elem_id="best-answer-output")
return answer_textbox, context_textbox
agent1_output, agent1_context_output = create_answer_row("Agent 1")
agent2_output, agent2_context_output = create_answer_row("Agent 2")
boolean_output, boolean_context_output = create_answer_row("Boolean")
tf_idf_output, tf_idf_context_output = create_answer_row("TF-IDF")
bm25_output, bm25_context_output = create_answer_row("BM25")
vision_output, vision_context_output = create_answer_row("Vision")
open_source_output, open_source_context_output = create_answer_row("Open Source")
boolean_mod_output, boolean_mod_context_output = create_answer_row("Boolean (Modified)")
tf_idf_mod_output, tf_idf_mod_context_output = create_answer_row("TF-IDF (Modified)")
bm25_mod_output, bm25_mod_context_output = create_answer_row("BM25 (Modified)")
vision_mod_output, vision_mod_context_output = create_answer_row("Vision (Modified)")
open_source_mod_output, open_source_mod_context_output = create_answer_row("Open Source (Modified)")
tf_idf_rrf_output, tf_idf_rrf_context_output = create_answer_row("TF-IDF + BM25 + Open RRF")
tf_idf_rrf_mod_output, tf_idf_rrf_mod_context_output = create_answer_row("TF-IDF + BM25 + Open RRF (Modified)")
tf_idf_rrf_combined_output, tf_idf_rrf_combined_context_output = create_answer_row("TF-IDF + BM25 + Open RRF (Combined)")
zero_shot_output, zero_shot_context_output = create_answer_row("Zero Shot")
submit_button.click(
fn=process_query,
inputs=query_input,
outputs=[
best_model_output,
best_answer_output,
agent1_output, agent1_context_output,
agent2_output, agent2_context_output,
boolean_output, boolean_context_output,
tf_idf_output, tf_idf_context_output,
bm25_output, bm25_context_output,
vision_output, vision_context_output,
open_source_output, open_source_context_output,
boolean_mod_output, boolean_mod_context_output,
tf_idf_mod_output, tf_idf_mod_context_output,
bm25_mod_output, bm25_mod_context_output,
vision_mod_output, vision_mod_context_output,
open_source_mod_output, open_source_mod_context_output,
tf_idf_rrf_output, tf_idf_rrf_context_output,
tf_idf_rrf_mod_output, tf_idf_rrf_mod_context_output,
tf_idf_rrf_combined_output, tf_idf_rrf_combined_context_output,
zero_shot_output, zero_shot_context_output
]
)
return interface
# Launch the interface
if __name__ == "__main__":
interface = create_interface()
interface.css = css
interface.launch()
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