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()