feat: implement hybrid similarity ranking model and API service integration for project comparison

api/services.py

@@ -1,162 +1,3 @@
-# # api/services.py
-
-# import pandas as pd
-
-# from src.similarity_engine import find_similar_projects
-# from src.preprocessing import extract_features
-
-
-# # =====================================================
-# # Main Analyze Service
-# # =====================================================
-# def analyze_project(
-#     title: str,
-#     description: str,
-#     abstract: str = "",
-#     features=None,
-#     top_k: int = 5
-# ):
-#     """
-#     Full project analysis service
-#     """
-
-#     if features is None:
-#         features = []
-
-#     # ---------------------------------------------
-#     # Build text for auto feature extraction
-#     # ---------------------------------------------
-#     full_text = f"{title}. {abstract}. {description}"
-
-#     auto_features = extract_features(full_text)
-
-#     # merge manual + auto
-#     merged = []
-#     seen = set()
-
-#     for item in features + auto_features:
-#         val = str(item).strip().lower()
-
-#         if val and val not in seen:
-#             seen.add(val)
-#             merged.append(val)
-
-#     # ---------------------------------------------
-#     # Run similarity engine
-#     # ---------------------------------------------
-#     results = find_similar_projects(
-#         title=title,
-#         description=f"{abstract} {description}",
-#         features=merged,
-#         top_k=top_k
-#     )
-
-#     # ---------------------------------------------
-#     # Convert dataframe to json records
-#     # ---------------------------------------------
-#     if isinstance(results, pd.DataFrame):
-#         rows = results.to_dict(orient="records")
-#     else:
-#         rows = []
-
-#     return {
-#         "extracted_features": merged,
-#         "results": rows
-#     }
-
-
-# api/services.py
-
-# import pandas as pd
-
-# from src.similarity_model import find_similar_projects
-# from src.similarity_model import extract_features
-
-
-# # =====================================================
-# # Main Analyze Service
-# # =====================================================
-# def analyze_project(
-#     title: str,
-#     description: str,
-#     abstract: str = "",
-#     features=None,
-#     top_k: int = 5
-# ):
-#     """
-#     Final clean API response
-#     Returns only user-needed metrics
-#     """
-
-#     if features is None:
-#         features = []
-
-#     # -------------------------------------------------
-#     # Build full text for automatic feature extraction
-#     # -------------------------------------------------
-#     full_text = f"{title}. {abstract}. {description}"
-
-#     auto_features = extract_features(full_text)
-
-#     # -------------------------------------------------
-#     # Merge manual + extracted features
-#     # -------------------------------------------------
-#     merged = []
-#     seen = set()
-
-#     for item in features + auto_features:
-#         val = str(item).strip().lower()
-
-#         if val and val not in seen:
-#             seen.add(val)
-#             merged.append(val)
-
-#     # -------------------------------------------------
-#     # Run similarity model
-#     # -------------------------------------------------
-#     results = find_similar_projects(
-#         title=title,
-#         description=f"{abstract} {description}",
-#         features=merged,
-#         top_k=top_k
-#     )
-
-#     # -------------------------------------------------
-#     # No results found
-#     # -------------------------------------------------
-#     if not isinstance(results, pd.DataFrame) or len(results) == 0:
-#         return {
-#             "message": "No similar projects found",
-#             "extracted_features": merged
-#         }
-
-#     # -------------------------------------------------
-#     # Take top matched project only
-#     # -------------------------------------------------
-#     top = results.iloc[0]
-
-#     return {
-#         "extracted_features": merged,
-
-#         "matched_features": top.get(
-#             "matched_features", []
-#         ),
-
-#         "unique_features": top.get(
-#             "unique_query_features", []
-#         ),
-
-#         "hybrid_similarity": round(
-#             float(top.get("hybrid_score", 0)),
-#             4
-#         ),
-
-#         "final_originality_score": round(
-#             float(top.get("originality_score", 0)),
-#             4
-#         )
-#     }
-
 import pandas as pd
 from fastapi import HTTPException
 
@@ -214,8 +55,9 @@ def analyze_project(
 
         top_projects.append({
             "project_title": row.get("project_title", ""),
+            "project_features": row.get("candidate_features", []),
             "matched_features": row.get("matched_features", []),
-            "unique_features": row.get("unique_query_features", []),
+            "unique_features": row.get("unique_candidate_features", []),
             "similarity_score": sim_percent,
             "final_originality_score": orig_score
         })

src/similarity_model/hybrid_ranker.py

@@ -287,7 +287,10 @@ def compare_single_candidate(
             feature_result["unique_a"],
 
         "unique_candidate_features":
-            feature_result["unique_b"]
+            feature_result["unique_b"],
+
+        "candidate_features":
+            candidate_features
     }