Update recommender/recommender.py

recommender/recommender.py

@@ -1,154 +1,154 @@
-import ast
-import re
-import pd
-import tempfile
-import os
-from intent_classification.fd_classification import find_intent
-from intent_classification.retrieval_classification import handle_query_classification,find_matching_card,generate_card_response_with_context
-from recommender.retrieval_ranking import generate_multi_queries,convert_to_direct_query_gradio,retrieve_and_rank_cards,generate_credit_card_recommendation_gemini,cross_encoder
-from data import eligibility_lookup,card_features_lookup
-from recommender.graph_retrieval_vectordb import generate_cypher,run_cypher_query,Neo4jConnectionError
-
-#function to pass the retrieved cards and generated response to the UI
-def recommend_cards_gradio(user_query, preferences, income, cibil, age,
-                           min_joining_fee, max_joining_fee,
-                           min_annual_fee, max_annual_fee,
-                           use_eligibility=True,include_cobranded=True):
-    try:
-        # print(user_query)
-        if(user_query):
-            result = handle_query_classification(user_query)
-
-            if result["intent"] == "no_retrieval":
-                return (
-                    f"<div style='background-color:#e3f2fd;padding:20px;border-radius:10px;'>"
-                    f"<pre style='white-space:pre-wrap;font-size:13px;color:#212121;'>{result['response']}</pre></div>",
-                    [["No retrieval required", "Answered using LLM"]],
-                    None,
-                    [],
-                    {},
-                    "Answered without retrieval"
-                )
-            elif result["intent"] == "specific":
-                matched_card = find_matching_card(user_query)
-                if matched_card:
-                    gemini_answer = generate_card_response_with_context(user_query, matched_card)
-                    card_name = matched_card["name"]
-                    card_desc = matched_card["description"]
-                    card_lookup = {card_name: card_desc}
-            
-                    # Constructing eligibility info if available
-                    eligibility_info = eligibility_lookup.get(card_name, "No eligibility or fee information available.")
-                    chat_history_entry = f"{card_name}:\n{card_desc}\n\nEligibility & Fees:\n{eligibility_info}"
-            
-                    return (
-                        f"<div style='background-color:#fffde7;padding:20px;border-radius:10px;'>"
-                        f"<pre style='white-space:pre-wrap;font-size:13px;color:#212121;'>{gemini_answer}</pre></div>",
-                        [["Specific card detected", card_name]],
-                        None,
-                        [],
-                        card_lookup,
-                        user_query 
-                    )
-                else:
-                    return (
-                        "<b style='color:red;'>Card mentioned not found in database.</b>",
-                        [["Card not found", "Try another card name."]],
-                        None,
-                        [],
-                        {},
-                        "Card not found"
-                    )
-        direct_query = convert_to_direct_query_gradio(user_query, preferences)
-        queries = generate_multi_queries(direct_query)
-
-        if cibil < 700 and use_eligibility:
-            query_intent = True
-        else:
-            query_intent = find_intent(user_query)
-            print(query_intent)
-        cypher_query = generate_cypher(direct_query, query_intent,include_cobranded)
-        print("Generated Cypher:\n", cypher_query)
-
-        try:
-            faiss_index, filtered_mapping = run_cypher_query(
-                user_query, cypher_query, use_eligibility,
-                income, cibil, age,
-                min_joining_fee, max_joining_fee,
-                min_annual_fee, max_annual_fee
-            )
-        except Neo4jConnectionError as graph_err:
-            return (
-                "<b style='color:red;'>Graph database connection failed. Please try again later.</b>",
-                [["Graph database error", str(graph_err)]],
-                None,
-                [],
-                {},
-                "Graph DB connection error"
-            )
-
-        cards = retrieve_and_rank_cards(faiss_index, filtered_mapping, direct_query, queries, top_k=10)
-        gemini_summary = generate_credit_card_recommendation_gemini(user_query, direct_query, cards)
-
-        if not cards:
-            return (
-                "<b style='color:red;'>No eligible cards found.</b>",
-                [["No eligible cards found", "Please try a different query or check your input values."]],
-                None,
-                [],
-                {},
-                "No eligible card found"
-            )
-
-        match = re.search(r"f\.name IN (\[.*?\])", cypher_query)
-        query_features = set(ast.literal_eval(match.group(1))) if match else set()
-
-        card_rows = []
-        for score, card in sorted(
-            zip(cross_encoder.predict([[direct_query, card["description"]] for card in cards]), cards),
-            reverse=True,
-            key=lambda x: x[0]
-        ):
-            card_name = card["name"]
-            card_desc = card["description"]
-            matched_features = query_features.intersection(card_features_lookup.get(card_name, set()))
-            feature_str = ", ".join(matched_features) if matched_features else "None"
-            card_rows.append([card_name, feature_str, card_desc])
-
-        
-        card_names = [row[0] for row in card_rows]
-        card_lookup = {row[0]: row[2] for row in card_rows}
-        top_card_html = f"""
-            <div style="
-                background-color: #fff3e0;
-                color: #212121;
-                border-radius: 16px;
-                padding: 20px;
-                border: 2px solid #ffa726;
-                box-shadow: 2px 2px 8px rgba(0,0,0,0.1);
-                margin-bottom: 16px;
-                font-family: sans-serif;
-                font-size: 8px;
-            ">
-                <pre style="white-space: pre-wrap; font-size: 13px; color: #212121;">{gemini_summary}</pre>
-            </div>
-        """
-
-        df_cards = pd.DataFrame(card_rows, columns=["Card Name", "Matched Features", "Description"])
-        filename = "recommended_cards.csv"
-        temp_dir = tempfile.gettempdir()
-        file_path = os.path.join(temp_dir, filename)
-        df_cards.to_csv(file_path, index=False)
-        
-        return top_card_html, card_rows, file_path, card_names, card_lookup, direct_query
-
-    except Exception as e:
-        print("Error:", e)
-        return (
-            "An unexpected error occurred. Please try again in a few minutes.",
-            [["Something went wrong", "Please try again."]],
-            None,
-            [],
-            {},
-            "Unexpected error occurred, please try again in a while"
+import ast
+import re
+import pandas as pd
+import tempfile
+import os
+from intent_classification.fd_classification import find_intent
+from intent_classification.retrieval_classification import handle_query_classification,find_matching_card,generate_card_response_with_context
+from recommender.retrieval_ranking import generate_multi_queries,convert_to_direct_query_gradio,retrieve_and_rank_cards,generate_credit_card_recommendation_gemini,cross_encoder
+from data import eligibility_lookup,card_features_lookup
+from recommender.graph_retrieval_vectordb import generate_cypher,run_cypher_query,Neo4jConnectionError
+
+#function to pass the retrieved cards and generated response to the UI
+def recommend_cards_gradio(user_query, preferences, income, cibil, age,
+                           min_joining_fee, max_joining_fee,
+                           min_annual_fee, max_annual_fee,
+                           use_eligibility=True,include_cobranded=True):
+    try:
+        # print(user_query)
+        if(user_query):
+            result = handle_query_classification(user_query)
+
+            if result["intent"] == "no_retrieval":
+                return (
+                    f"<div style='background-color:#e3f2fd;padding:20px;border-radius:10px;'>"
+                    f"<pre style='white-space:pre-wrap;font-size:13px;color:#212121;'>{result['response']}</pre></div>",
+                    [["No retrieval required", "Answered using LLM"]],
+                    None,
+                    [],
+                    {},
+                    "Answered without retrieval"
+                )
+            elif result["intent"] == "specific":
+                matched_card = find_matching_card(user_query)
+                if matched_card:
+                    gemini_answer = generate_card_response_with_context(user_query, matched_card)
+                    card_name = matched_card["name"]
+                    card_desc = matched_card["description"]
+                    card_lookup = {card_name: card_desc}
+            
+                    # Constructing eligibility info if available
+                    eligibility_info = eligibility_lookup.get(card_name, "No eligibility or fee information available.")
+                    chat_history_entry = f"{card_name}:\n{card_desc}\n\nEligibility & Fees:\n{eligibility_info}"
+            
+                    return (
+                        f"<div style='background-color:#fffde7;padding:20px;border-radius:10px;'>"
+                        f"<pre style='white-space:pre-wrap;font-size:13px;color:#212121;'>{gemini_answer}</pre></div>",
+                        [["Specific card detected", card_name]],
+                        None,
+                        [],
+                        card_lookup,
+                        user_query 
+                    )
+                else:
+                    return (
+                        "<b style='color:red;'>Card mentioned not found in database.</b>",
+                        [["Card not found", "Try another card name."]],
+                        None,
+                        [],
+                        {},
+                        "Card not found"
+                    )
+        direct_query = convert_to_direct_query_gradio(user_query, preferences)
+        queries = generate_multi_queries(direct_query)
+
+        if cibil < 700 and use_eligibility:
+            query_intent = True
+        else:
+            query_intent = find_intent(user_query)
+            print(query_intent)
+        cypher_query = generate_cypher(direct_query, query_intent,include_cobranded)
+        print("Generated Cypher:\n", cypher_query)
+
+        try:
+            faiss_index, filtered_mapping = run_cypher_query(
+                user_query, cypher_query, use_eligibility,
+                income, cibil, age,
+                min_joining_fee, max_joining_fee,
+                min_annual_fee, max_annual_fee
+            )
+        except Neo4jConnectionError as graph_err:
+            return (
+                "<b style='color:red;'>Graph database connection failed. Please try again later.</b>",
+                [["Graph database error", str(graph_err)]],
+                None,
+                [],
+                {},
+                "Graph DB connection error"
+            )
+
+        cards = retrieve_and_rank_cards(faiss_index, filtered_mapping, direct_query, queries, top_k=10)
+        gemini_summary = generate_credit_card_recommendation_gemini(user_query, direct_query, cards)
+
+        if not cards:
+            return (
+                "<b style='color:red;'>No eligible cards found.</b>",
+                [["No eligible cards found", "Please try a different query or check your input values."]],
+                None,
+                [],
+                {},
+                "No eligible card found"
+            )
+
+        match = re.search(r"f\.name IN (\[.*?\])", cypher_query)
+        query_features = set(ast.literal_eval(match.group(1))) if match else set()
+
+        card_rows = []
+        for score, card in sorted(
+            zip(cross_encoder.predict([[direct_query, card["description"]] for card in cards]), cards),
+            reverse=True,
+            key=lambda x: x[0]
+        ):
+            card_name = card["name"]
+            card_desc = card["description"]
+            matched_features = query_features.intersection(card_features_lookup.get(card_name, set()))
+            feature_str = ", ".join(matched_features) if matched_features else "None"
+            card_rows.append([card_name, feature_str, card_desc])
+
+        
+        card_names = [row[0] for row in card_rows]
+        card_lookup = {row[0]: row[2] for row in card_rows}
+        top_card_html = f"""
+            <div style="
+                background-color: #fff3e0;
+                color: #212121;
+                border-radius: 16px;
+                padding: 20px;
+                border: 2px solid #ffa726;
+                box-shadow: 2px 2px 8px rgba(0,0,0,0.1);
+                margin-bottom: 16px;
+                font-family: sans-serif;
+                font-size: 8px;
+            ">
+                <pre style="white-space: pre-wrap; font-size: 13px; color: #212121;">{gemini_summary}</pre>
+            </div>
+        """
+
+        df_cards = pd.DataFrame(card_rows, columns=["Card Name", "Matched Features", "Description"])
+        filename = "recommended_cards.csv"
+        temp_dir = tempfile.gettempdir()
+        file_path = os.path.join(temp_dir, filename)
+        df_cards.to_csv(file_path, index=False)
+        
+        return top_card_html, card_rows, file_path, card_names, card_lookup, direct_query
+
+    except Exception as e:
+        print("Error:", e)
+        return (
+            "An unexpected error occurred. Please try again in a few minutes.",
+            [["Something went wrong", "Please try again."]],
+            None,
+            [],
+            {},
+            "Unexpected error occurred, please try again in a while"
         )