Added all the answers
Browse files
app.py
CHANGED
@@ -51,31 +51,54 @@ def process_query(query):
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tf_idf_ranking_modified, bm25_ranking_modified, open_source_ranking_modified
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boolean_context = miniWikiCollectionDict[boolean_ranking[0]]
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tf_idf_context = miniWikiCollectionDict[tf_idf_ranking[0]]
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bm25_context = miniWikiCollectionDict[str(bm25_ranking[0])]
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vision_context = miniWikiCollectionDict[vision_ranking[0]]
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open_source_context = miniWikiCollectionDict[open_source_ranking[0]]
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tf_idf_bm25_open_RRF_Ranking_context = miniWikiCollectionDict[tf_idf_bm25_open_RRF_Ranking[0]]
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agent2_context = article
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agent1_answer = generate_answer_withContext(query, agent1_context)
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agent2_answer = generate_answer_withContext(query, agent2_context)
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boolean_answer = generate_answer_withContext(query, boolean_context)
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tf_idf_answer = generate_answer_withContext(query, tf_idf_context)
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bm25_answer = generate_answer_withContext(query, bm25_context)
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vision_answer = generate_answer_withContext(query, vision_context)
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open_source_answer = generate_answer_withContext(query, open_source_context)
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tf_idf_bm25_open_RRF_Ranking_answer = generate_answer_withContext(query, tf_idf_bm25_open_RRF_Ranking_context)
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zeroShot = generate_answer_zeroShot(query)
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# Ranking the best answer
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rankerAgentInput = {
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"query": query,
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"bm25": bm25_answer,
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"vision": vision_answer,
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"open_source": open_source_answer,
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"tf_idf_bm25_open_RRF_Ranking": tf_idf_bm25_open_RRF_Ranking_answer,
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"
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}
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best_model, best_answer = rankerAgent(rankerAgentInput)
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# Gradio interface
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gr.Textbox(label="
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)
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if __name__ == "__main__":
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-
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tf_idf_ranking_modified, bm25_ranking_modified, open_source_ranking_modified
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)
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agent1_context = wiki_data[0]
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agent2_context = article
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boolean_context = miniWikiCollectionDict[boolean_ranking[0]]
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tf_idf_context = miniWikiCollectionDict[tf_idf_ranking[0]]
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bm25_context = miniWikiCollectionDict[str(bm25_ranking[0])]
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vision_context = miniWikiCollectionDict[vision_ranking[0]]
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open_source_context = miniWikiCollectionDict[open_source_ranking[0]]
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boolean_context_modified = miniWikiCollectionDict[boolean_ranking_modified[0]]
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tf_idf_context_modified = miniWikiCollectionDict[tf_idf_ranking_modified[0]]
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bm25_context = miniWikiCollectionDict[str(bm25_ranking_modified[0])]
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vision_context_modified = miniWikiCollectionDict[vision_ranking_modified[0]]
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open_source_context_modified = miniWikiCollectionDict[open_source_ranking_modified[0]]
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tf_idf_bm25_open_RRF_Ranking_context = miniWikiCollectionDict[tf_idf_bm25_open_RRF_Ranking[0]]
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tf_idf_bm25_open_RRF_Ranking_modified_context = miniWikiCollectionDict[tf_idf_bm25_open_RRF_Ranking_modified[0]]
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tf_idf_bm25_open_RRF_Ranking_combined_context = miniWikiCollectionDict[tf_idf_bm25_open_RRF_Ranking_combined[0]]
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#Generating answers
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agent1_answer = generate_answer_withContext(query, agent1_context)
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agent2_answer = generate_answer_withContext(query, agent2_context)
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boolean_answer = generate_answer_withContext(query, boolean_context)
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tf_idf_answer = generate_answer_withContext(query, tf_idf_context)
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bm25_answer = generate_answer_withContext(query, bm25_context)
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vision_answer = generate_answer_withContext(query, vision_context)
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open_source_answer = generate_answer_withContext(query, open_source_context)
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boolean_answer_modified = generate_answer_withContext(modified_query, boolean_context_modified)
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tf_idf_answer_modified = generate_answer_withContext(modified_query, tf_idf_context_modified)
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bm25_answer_modified = generate_answer_withContext(modified_query, bm25_context)
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vision_answer_modified = generate_answer_withContext(modified_query, vision_context_modified)
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open_source_answer_modified = generate_answer_withContext(modified_query, open_source_context_modified)
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tf_idf_bm25_open_RRF_Ranking_answer = generate_answer_withContext(query, tf_idf_bm25_open_RRF_Ranking_context)
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tf_idf_bm25_open_RRF_Ranking_modified_answer = generate_answer_withContext(modified_query, tf_idf_bm25_open_RRF_Ranking_modified_context)
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tf_idf_bm25_open_RRF_Ranking_combined_answer = generate_answer_withContext(query, tf_idf_bm25_open_RRF_Ranking_combined_context)
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zeroShot = generate_answer_zeroShot(query)
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# Ranking the best answer
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rankerAgentInput = {
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"query": query,
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"bm25": bm25_answer,
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"vision": vision_answer,
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"open_source": open_source_answer,
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"boolean_modified": boolean_answer_modified,
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"tf_idf_modified": tf_idf_answer_modified,
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"bm25_modified": bm25_answer_modified,
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"vision_modified": vision_answer_modified,
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"open_source_modified": open_source_answer_modified,
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"tf_idf_bm25_open_RRF_Ranking": tf_idf_bm25_open_RRF_Ranking_answer,
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"tf_idf_bm25_open_RRF_Ranking_modified": tf_idf_bm25_open_RRF_Ranking_modified_answer,
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"tf_idf_bm25_open_RRF_Ranking_combined": tf_idf_bm25_open_RRF_Ranking_combined_answer,
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"zeroShot": zeroShot
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}
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best_model, best_answer = rankerAgent(rankerAgentInput)
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all_answers = {
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"Agent 1": agent1_answer,
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"Agent 2": agent2_answer,
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"Boolean": boolean_answer,
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"TF-IDF": tf_idf_answer,
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"BM25": bm25_answer,
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"Vision": vision_answer,
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"Open Source": open_source_answer,
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"Boolean (Modified)": boolean_answer_modified,
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"TF-IDF (Modified)": tf_idf_answer_modified,
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"BM25 (Modified)": bm25_answer_modified,
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"Vision (Modified)": vision_answer_modified,
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"Open Source (Modified)": open_source_answer_modified,
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"TF-IDF + BM25 + Open RRF": tf_idf_bm25_open_RRF_Ranking_answer,
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"TF-IDF + BM25 + Open RRF (Modified)": tf_idf_bm25_open_RRF_Ranking_modified_answer,
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"TF-IDF + BM25 + Open RRF (Combined)": tf_idf_bm25_open_RRF_Ranking_combined_answer,
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"Zero Shot": zeroShot,
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}
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return best_model, best_answer, all_answers
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# Gradio interface
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def create_interface():
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with gr.Blocks() as interface:
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gr.Markdown("# Query Answering System")
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gr.Markdown("Enter a query to get the best model and the best answer using multiple retrieval models and ranking techniques.")
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query_input = gr.Textbox(label="Enter your query")
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with gr.Row():
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best_model_output = gr.Textbox(label="Best Model")
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best_answer_output = gr.Textbox(label="Best Answer")
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gr.Markdown("---") # Horizontal line
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gr.Markdown("## All Answers")
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with gr.Row():
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agent1_output = gr.Textbox(label="Agent 1")
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agent2_output = gr.Textbox(label="Agent 2")
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boolean_output = gr.Textbox(label="Boolean")
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tf_idf_output = gr.Textbox(label="TF-IDF")
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bm25_output = gr.Textbox(label="BM25")
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with gr.Row():
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vision_output = gr.Textbox(label="Vision")
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open_source_output = gr.Textbox(label="Open Source")
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boolean_mod_output = gr.Textbox(label="Boolean (Modified)")
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tf_idf_mod_output = gr.Textbox(label="TF-IDF (Modified)")
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bm25_mod_output = gr.Textbox(label="BM25 (Modified)")
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with gr.Row():
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vision_mod_output = gr.Textbox(label="Vision (Modified)")
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open_source_mod_output = gr.Textbox(label="Open Source (Modified)")
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tf_idf_rrf_output = gr.Textbox(label="TF-IDF + BM25 + Open RRF")
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tf_idf_rrf_mod_output = gr.Textbox(label="TF-IDF + BM25 + Open RRF (Modified)")
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tf_idf_rrf_combined_output = gr.Textbox(label="TF-IDF + BM25 + Open RRF (Combined)")
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zero_shot_output = gr.Textbox(label="Zero Shot")
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gr.Button("Submit").click(
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fn=process_query,
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inputs=query_input,
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outputs=[
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best_model_output,
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best_answer_output,
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agent1_output, agent2_output,
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boolean_output, tf_idf_output, bm25_output,
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vision_output, open_source_output,
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boolean_mod_output, tf_idf_mod_output, bm25_mod_output,
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vision_mod_output, open_source_mod_output,
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tf_idf_rrf_output, tf_idf_rrf_mod_output,
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tf_idf_rrf_combined_output, zero_shot_output,
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]
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)
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return interface
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if __name__ == "__main__":
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create_interface().launch()
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