Add application file

app.py

@@ -0,0 +1,211 @@
+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."
+    )
+
+# Interface creation
+def create_interface():
+    with gr.Blocks() as interface:
+        query_input = gr.Textbox(label="Enter your query")
+        best_model_output = gr.Textbox(label="Best Model", interactive=False)
+        best_answer_output = gr.Textbox(label="Best Answer", interactive=False)
+
+        def create_answer_row(label):
+            with gr.Row():
+                answer_textbox = gr.Textbox(label=f"{label} Answer", interactive=False)
+                context_button = gr.Button(f"Show {label} Context")
+                context_textbox = gr.Textbox(label=f"{label} Context", visible=False)
+
+                # Event to show the context
+                context_button.click(
+                    fn=lambda x: gr.update(visible=True, value=x),
+                    inputs=None,
+                    outputs=context_textbox
+                )
+                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_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")
+
+        gr.Button("Submit").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_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.launch()