IR-IIITH/MultiAgent-OpenDomain-QnA-System
1
1import gradio as gr2import json3 4# Import your modules here5from Agents.togetherAIAgent import generate_article_from_query6from Agents.wikiAgent import get_wiki_data7from Agents.rankerAgent import rankerAgent8from Query_Modification.QueryModification import query_Modifier, getKeywords9from Ranking.RRF.RRF_implementation import reciprocal_rank_fusion_three, reciprocal_rank_fusion_six10from Retrieval.tf_idf import tf_idf_pipeline11from Retrieval.bm25 import bm25_pipeline12from Retrieval.vision import vision_pipeline13from Retrieval.openSource import open_source_pipeline14from Baseline.boolean import boolean_pipeline15from AnswerGeneration.getAnswer import generate_answer_withContext, generate_answer_zeroShot16 17# Load miniWikiCollection18miniWikiCollection = json.load(open('Datasets/mini_wiki_collection.json', 'r'))19miniWikiCollectionDict = {wiki['wikipedia_id']: " ".join(wiki['text']) for wiki in miniWikiCollection}20 21def process_query(query):22 # Query modification23 modified_query = query_Modifier(query)24 25 # Context Generation26 article = generate_article_from_query(query)27 28 # Keyword Extraction and getting context from Wiki29 keywords = getKeywords(query)30 wiki_data = get_wiki_data(keywords)31 32 # Retrieve rankings33 boolean_ranking = boolean_pipeline(query)34 tf_idf_ranking = tf_idf_pipeline(query)35 bm25_ranking = bm25_pipeline(query)36 vision_ranking = vision_pipeline(query)37 open_source_ranking = open_source_pipeline(query)38 39 # Modified queries40 boolean_ranking_modified = boolean_pipeline(modified_query)41 tf_idf_ranking_modified = tf_idf_pipeline(modified_query)42 bm25_ranking_modified = bm25_pipeline(modified_query)43 vision_ranking_modified = vision_pipeline(modified_query)44 open_source_ranking_modified = open_source_pipeline(modified_query)45 46 # RRF rankings47 tf_idf_bm25_open_RRF_Ranking = reciprocal_rank_fusion_three(tf_idf_ranking, bm25_ranking, open_source_ranking)48 tf_idf_bm25_open_RRF_Ranking_modified = reciprocal_rank_fusion_three(tf_idf_ranking_modified, bm25_ranking_modified, open_source_ranking_modified)49 tf_idf_bm25_open_RRF_Ranking_combined = reciprocal_rank_fusion_six(50 tf_idf_ranking, bm25_ranking, open_source_ranking,51 tf_idf_ranking_modified, bm25_ranking_modified, open_source_ranking_modified52 )53 54 try:55 agent1_context = wiki_data[0]56 except:57 agent1_context = "Can't find a Wiki article for this query."58 59 agent2_context = article60 61 try:62 boolean_context = miniWikiCollectionDict[boolean_ranking[0]]63 except:64 boolean_context = "Can't find a matching document for this query."65 66 tf_idf_context = miniWikiCollectionDict[tf_idf_ranking[0]]67 bm25_context = miniWikiCollectionDict[str(bm25_ranking[0])]68 vision_context = miniWikiCollectionDict[vision_ranking[0]]69 open_source_context = miniWikiCollectionDict[open_source_ranking[0]]70 71 boolean_context_modified = miniWikiCollectionDict[boolean_ranking_modified[0]]72 tf_idf_context_modified = miniWikiCollectionDict[tf_idf_ranking_modified[0]]73 bm25_context_modified = miniWikiCollectionDict[str(bm25_ranking_modified[0])]74 vision_context_modified = miniWikiCollectionDict[vision_ranking_modified[0]]75 open_source_context_modified = miniWikiCollectionDict[open_source_ranking_modified[0]]76 77 tf_idf_bm25_open_RRF_Ranking_context = miniWikiCollectionDict[tf_idf_bm25_open_RRF_Ranking[0]]78 tf_idf_bm25_open_RRF_Ranking_modified_context = miniWikiCollectionDict[tf_idf_bm25_open_RRF_Ranking_modified[0]]79 tf_idf_bm25_open_RRF_Ranking_combined_context = miniWikiCollectionDict[tf_idf_bm25_open_RRF_Ranking_combined[0]]80 81 # Generating answers82 agent1_answer = generate_answer_withContext(query, agent1_context)83 agent2_answer = generate_answer_withContext(query, agent2_context)84 85 boolean_answer = generate_answer_withContext(query, boolean_context)86 tf_idf_answer = generate_answer_withContext(query, tf_idf_context)87 bm25_answer = generate_answer_withContext(query, bm25_context)88 vision_answer = generate_answer_withContext(query, vision_context)89 open_source_answer = generate_answer_withContext(query, open_source_context)90 91 boolean_answer_modified = generate_answer_withContext(modified_query, boolean_context_modified)92 tf_idf_answer_modified = generate_answer_withContext(modified_query, tf_idf_context_modified)93 bm25_answer_modified = generate_answer_withContext(modified_query, bm25_context_modified)94 vision_answer_modified = generate_answer_withContext(modified_query, vision_context_modified)95 open_source_answer_modified = generate_answer_withContext(modified_query, open_source_context_modified)96 97 tf_idf_bm25_open_RRF_Ranking_answer = generate_answer_withContext(query, tf_idf_bm25_open_RRF_Ranking_context)98 tf_idf_bm25_open_RRF_Ranking_modified_answer = generate_answer_withContext(modified_query, tf_idf_bm25_open_RRF_Ranking_modified_context)99 tf_idf_bm25_open_RRF_Ranking_combined_answer = generate_answer_withContext(query, tf_idf_bm25_open_RRF_Ranking_combined_context)100 101 zeroShot = generate_answer_zeroShot(query)102 103 # Ranking the best answer104 rankerAgentInput = {105 "query": query,106 "agent1": agent1_answer,107 "agent2": agent2_answer,108 "boolean": boolean_answer,109 "tf_idf": tf_idf_answer,110 "bm25": bm25_answer,111 "vision": vision_answer,112 "open_source": open_source_answer,113 "boolean_modified": boolean_answer_modified,114 "tf_idf_modified": tf_idf_answer_modified,115 "bm25_modified": bm25_answer_modified,116 "vision_modified": vision_answer_modified,117 "open_source_modified": open_source_answer_modified,118 "tf_idf_bm25_open_RRF_Ranking": tf_idf_bm25_open_RRF_Ranking_answer,119 "tf_idf_bm25_open_RRF_Ranking_modified": tf_idf_bm25_open_RRF_Ranking_modified_answer,120 "tf_idf_bm25_open_RRF_Ranking_combined": tf_idf_bm25_open_RRF_Ranking_combined_answer,121 "zeroShot": zeroShot122 }123 124 best_model, best_answer = rankerAgent(rankerAgentInput)125 126 return (127 best_model,128 best_answer,129 agent1_answer, agent1_context,130 agent2_answer, agent2_context,131 boolean_answer, boolean_context,132 tf_idf_answer, tf_idf_context,133 bm25_answer, bm25_context,134 vision_answer, vision_context,135 open_source_answer, open_source_context,136 boolean_answer_modified, boolean_context_modified,137 tf_idf_answer_modified, tf_idf_context_modified,138 bm25_answer_modified, bm25_context_modified,139 vision_answer_modified, vision_context_modified,140 open_source_answer_modified, open_source_context_modified,141 tf_idf_bm25_open_RRF_Ranking_answer, tf_idf_bm25_open_RRF_Ranking_context,142 tf_idf_bm25_open_RRF_Ranking_modified_answer, tf_idf_bm25_open_RRF_Ranking_modified_context,143 tf_idf_bm25_open_RRF_Ranking_combined_answer, tf_idf_bm25_open_RRF_Ranking_combined_context,144 zeroShot, "Zero-shot doesn't have a context."145 )146 147# CSS Styling for the fancy effects148css = """149#fancy-column {150 background: linear-gradient(135deg, #1a242f, #2b3a44); /* Dark blue-gray gradient background */151 padding: 20px;152 border-radius: 15px;153}154 155#query-input, #submit-button, #best-model-output, #best-answer-output {156 border-radius: 10px; /* Rounded corners */157 box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3); /* Darker shadow for better contrast */158 background-color: #34495e; /* Dark background for inputs */159 color: #ecf0f1; /* Light text for good readability */160}161 162#query-input:focus, #submit-button:focus, #best-model-output:focus, #best-answer-output:focus {163 outline: none;164 border: 2px solid #7f8c8d; /* Subtle accent border on focus */165}166 167#submit-button {168 background-color: #16a085; /* Muted teal color for button */169 color: #ecf0f1; /* Light text for button */170 font-weight: bold;171 padding: 10px;172}173 174#submit-button:hover {175 background-color: #1abc9c; /* Slightly lighter teal on hover */176}177 178#best-model-output, #best-answer-output {179 background-color: #2c3e50; /* Darker background for output boxes */180}181 182#best-model-output label, #best-answer-output label, #query-input label {183 color: #ecf0f1; /* Light text for labels */184}185"""186 187 188 189# Interface creation190def create_interface():191 with gr.Blocks() as interface:192 with gr.Column(elem_id="fancy-column", scale=3): # Fancy column with extra styling193 with gr.Row():194 query_input = gr.Textbox(label="Enter your query", scale=3, elem_id="query-input")195 submit_button = gr.Button("Submit", scale=1, elem_id="submit-button")196 197 # Adjusting the spacing between the output fields198 with gr.Row():199 best_model_output = gr.Textbox(label="Best Model", interactive=False, scale=1.5, elem_id="best-model-output")200 best_answer_output = gr.Textbox(label="Best Answer", interactive=False, scale=1.5, elem_id="best-answer-output")201 202 with gr.Column():203 # Function to create a row for answers and contexts204 def create_answer_row(label):205 if label == "Agent 1":206 label = "Wiki Search"207 elif label == "Agent 2":208 label = "Llama Context Generation"209 elif label == "Open Source Answer":210 label = 'MiniLM Text Embedding model'211 elif label == "Open Source (Modified)":212 label = 'MiniLM Text Embedding model (Modified)'213 elif label == "TF-IDF + BM25 + Open RRF":214 label = "RRF (TF-IDF + BM25 + MiniLM)"215 elif label == "TF-IDF + BM25 + Open RRF (Modified)":216 label = "RRF (TF-IDF + BM25 + MiniLM) (Modified)"217 elif label == "TF-IDF + BM25 + Open RRF (Combined)":218 label = "RRF (TF-IDF + BM25 + MiniLM) (Combined)"219 with gr.Row():220 answer_textbox = gr.Textbox(label=f"{label} Answer", interactive=False, scale=1.2, elem_id="best-model-output")221 context_textbox = gr.Textbox(label=f"{label} Context", scale=1.8, elem_id="best-answer-output")222 223 return answer_textbox, context_textbox224 225 agent1_output, agent1_context_output = create_answer_row("Agent 1")226 agent2_output, agent2_context_output = create_answer_row("Agent 2")227 boolean_output, boolean_context_output = create_answer_row("Boolean")228 tf_idf_output, tf_idf_context_output = create_answer_row("TF-IDF")229 bm25_output, bm25_context_output = create_answer_row("BM25")230 vision_output, vision_context_output = create_answer_row("Vision")231 open_source_output, open_source_context_output = create_answer_row("Open Source")232 233 boolean_mod_output, boolean_mod_context_output = create_answer_row("Boolean (Modified)")234 tf_idf_mod_output, tf_idf_mod_context_output = create_answer_row("TF-IDF (Modified)")235 bm25_mod_output, bm25_mod_context_output = create_answer_row("BM25 (Modified)")236 vision_mod_output, vision_mod_context_output = create_answer_row("Vision (Modified)")237 open_source_mod_output, open_source_mod_context_output = create_answer_row("Open Source (Modified)")238 239 tf_idf_rrf_output, tf_idf_rrf_context_output = create_answer_row("TF-IDF + BM25 + Open RRF")240 tf_idf_rrf_mod_output, tf_idf_rrf_mod_context_output = create_answer_row("TF-IDF + BM25 + Open RRF (Modified)")241 tf_idf_rrf_combined_output, tf_idf_rrf_combined_context_output = create_answer_row("TF-IDF + BM25 + Open RRF (Combined)")242 243 zero_shot_output, zero_shot_context_output = create_answer_row("Zero Shot")244 245 submit_button.click(246 fn=process_query,247 inputs=query_input,248 outputs=[249 best_model_output,250 best_answer_output,251 agent1_output, agent1_context_output,252 agent2_output, agent2_context_output,253 boolean_output, boolean_context_output,254 tf_idf_output, tf_idf_context_output,255 bm25_output, bm25_context_output,256 vision_output, vision_context_output,257 open_source_output, open_source_context_output,258 boolean_mod_output, boolean_mod_context_output,259 tf_idf_mod_output, tf_idf_mod_context_output,260 bm25_mod_output, bm25_mod_context_output,261 vision_mod_output, vision_mod_context_output,262 open_source_mod_output, open_source_mod_context_output,263 tf_idf_rrf_output, tf_idf_rrf_context_output,264 tf_idf_rrf_mod_output, tf_idf_rrf_mod_context_output,265 tf_idf_rrf_combined_output, tf_idf_rrf_combined_context_output,266 zero_shot_output, zero_shot_context_output267 ]268 )269 270 return interface271 272# Launch the interface273if __name__ == "__main__":274 interface = create_interface()275 interface.css = css276 interface.launch()277