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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
)
agent1_context = wiki_data[0]
agent2_context = article
boolean_context = miniWikiCollectionDict[boolean_ranking[0]]
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."
)
# Gradio interface
def create_interface():
with gr.Blocks() as interface:
gr.Markdown("# Query Answering System")
gr.Markdown("Enter a query to get the best model and the best answer using multiple retrieval models and ranking techniques.")
query_input = gr.Textbox(label="Enter your query")
with gr.Row():
best_model_output = gr.Textbox(label="Best Model")
best_answer_output = gr.Textbox(label="Best Answer")
gr.Markdown("---") # Horizontal line
gr.Markdown("## All Answers and Contexts")
def create_answer_row(label, context):
with gr.Row():
answer_textbox = gr.Textbox(label=f"{label} Answer", interactive=False).style(container=True)
context_button = gr.Button(f"Show {label} Context")
context_textbox = gr.Textbox(label=f"{label} Context", visible=False).style(container=True)
context_button.click(
fn=lambda: gr.update(visible=True, value=context),
inputs=[],
outputs=context_textbox,
)
return answer_textbox, context_textbox
with gr.Row():
agent1_output, agent1_context_box = create_answer_row("Agent 1", agent1_context)
agent2_output, agent2_context_box = create_answer_row("Agent 2", agent_text_context)
boolean_output, boolean_context_box = create_answer_row("Boolean", boolean_context)
tf_idf_output, tf_idf_context_box = create_answer_row("TF-IDF", tf_idf_context)
bm25_output, bm25_context_box = create_answer_row("BM25", bm25_context)
vision_output, vision_context_box = create_answer_row("Vision", vision_context)
open_source_output, open_source_context_box = create_answer_row("Open Source", open_source_context)
with gr.Row():
boolean_mod_output, boolean_mod_context_box = create_answer_row("Boolean (Modified)", boolean_context_modified)
tf_idf_mod_output, tf_idf_mod_context_box = create_answer_row("TF-IDF (Modified)", tf_idf_context_modified)
bm25_mod_output, bm25_mod_context_box = create_answer_row("BM25 (Modified)", bm25_context_modified)
vision_mod_output, vision_mod_context_box = create_answer_row("Vision (Modified)", vision_context_modified)
open_source_mod_output, open_source_mod_context_box = create_answer_row("Open Source (Modified)", open_source_context_modified)
with gr.Row():
tf_idf_rrf_output, tf_idf_rrf_context_box = create_answer_row("TF-IDF + BM25 + Open RRF", tf_idf_bm25_open_RRF_Ranking_context)
tf_idf_rrf_mod_output, tf_idf_rrf_mod_context_box = create_answer_row("TF-IDF + BM25 + Open RRF (Modified)", tf_idf_bm25_open_RRF_Ranking_modified_context)
tf_idf_rrf_combined_output, tf_idf_rrf_combined_context_box = create_answer_row("TF-IDF + BM25 + Open RRF (Combined)", tf_idf_bm25_open_RRF_Ranking_combined_context)
with gr.Row():
zero_shot_output, zero_shot_context_box = create_answer_row("Zero Shot", "Zero-shot doesn't have a context.")
gr.Button("Submit").click(
fn=process_query,
inputs=query_input,
outputs=[
best_model_output,
best_answer_output,
agent1_output, agent1_context_box,
agent2_output, agent2_context_box,
boolean_output, boolean_context_box,
tf_idf_output, tf_idf_context_box,
bm25_output, bm25_context_box,
vision_output, vision_context_box,
open_source_output, open_source_context_box,
boolean_mod_output, boolean_mod_context_box,
tf_idf_mod_output, tf_idf_mod_context_box,
bm25_mod_output, bm25_mod_context_box,
vision_mod_output, vision_mod_context_box,
open_source_mod_output, open_source_context_box,
tf_idf_rrf_output, tf_idf_rrf_context_box,
tf_idf_rrf_mod_output, tf_idf_rrf_mod_context_box,
tf_idf_rrf_combined_output, tf_idf_rrf_combined_context_box,
zero_shot_output, zero_shot_context_box,
]
)
return interface
if __name__ == "__main__":
create_interface().launch()
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