Update: services.py

api/services.py

@@ -1,100 +1,100 @@
-import pandas as pd
-from fastapi import HTTPException
-
-from src.similarity_model import find_similar_projects
-from src.similarity_model import extract_features
-
-
-def analyze_project(
-    title: str,
-    description: str,
-    abstract: str = "",
-    features=None,
-    top_k: int = 5
-):
-
-    if features is None:
-        features = []
-
-    full_text = f"{title}. {abstract}. {description}"
-
-    auto_features = extract_features(full_text)
-
-    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)
-
-    results = find_similar_projects(
-        title=title,
-        description=f"{abstract} {description}",
-        features=merged,
-        top_k=top_k
-    )
-
-    if not isinstance(results, pd.DataFrame) or len(results) == 0:
-        return {
-            "message": "No similar projects found",
-            "extracted_features": merged,
-            "overall_originality_score": 100.0
-        }
-
-    # -----------------------------------
-    # رجع Top K كله
-    # -----------------------------------
-    top_projects = []
-
-    for _, row in results.iterrows():
-        orig_score = round(float(row.get("originality_score", 0)), 2)
-        sim_percent = round(float(row.get("hybrid_score", 0)) * 100, 2)
-
-        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_candidate_features", []),
-            "similarity_score": sim_percent,
-            "final_originality_score": orig_score
-        })
-
-    # Overall = worst-case originality (against the most similar project)
-    overall_originality_score = top_projects[0]["final_originality_score"]
-
-    return {
-        "extracted_features": merged,
-        "overall_originality_score": overall_originality_score,
-        "top_similar_projects": top_projects
-    }
-
-
-
-def chat_with_llm(user_id: str, message: str):
-    try:
-        from src.recommendation_engine.chatbot_engine import chatbot
-        from src.recommendation_engine.llm_client import LLMProviderError
-    except Exception as exc:
-        raise HTTPException(
-            status_code=503,
-            detail=f"LLM service could not start: {exc}"
-        )
-
-    try:
-        response = chatbot(
-            user_id=user_id,
-            user_input=message
-        )
-    except LLMProviderError as exc:
-        raise HTTPException(
-            status_code=exc.status_code,
-            detail=exc.message
-        )
-
-    return {
-        "user_id": user_id,
-        "response": response
-    }
+import pandas as pd
+from fastapi import HTTPException
+
+from src.similarity_model import find_similar_projects
+from src.similarity_model import extract_features
+
+
+def analyze_project(
+    title: str,
+    description: str,
+    abstract: str = "",
+    features=None,
+    top_k: int = 5
+):
+
+    if features is None:
+        features = []
+
+    full_text = f"{title}. {abstract}. {description}"
+
+    auto_features = extract_features(full_text)
+
+    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)
+
+    results = find_similar_projects(
+        title=title,
+        description=f"{abstract} {description}",
+        features=merged,
+        top_k=top_k
+    )
+
+    if not isinstance(results, pd.DataFrame) or len(results) == 0:
+        return {
+            "message": "No similar projects found",
+            "extracted_features": merged,
+            "overall_originality_score": 100.0
+        }
+
+    # -----------------------------------
+    # رجع Top K كله
+    # -----------------------------------
+    top_projects = []
+
+    for _, row in results.iterrows():
+        orig_score = round(float(row.get("originality_score", 0)), 2)
+        sim_percent = round(float(row.get("hybrid_score", 0)) * 100, 2)
+
+        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_candidate_features", []),
+            "similarity_score": sim_percent,
+            "final_originality_score": orig_score
+        })
+
+    # Overall = largest (maximum) originality score among the top matches
+    overall_originality_score = max(p["final_originality_score"] for p in top_projects)
+
+    return {
+        "extracted_features": merged,
+        "overall_originality_score": overall_originality_score,
+        "top_similar_projects": top_projects
+    }
+
+
+
+def chat_with_llm(user_id: str, message: str):
+    try:
+        from src.recommendation_engine.chatbot_engine import chatbot
+        from src.recommendation_engine.llm_client import LLMProviderError
+    except Exception as exc:
+        raise HTTPException(
+            status_code=503,
+            detail=f"LLM service could not start: {exc}"
+        )
+
+    try:
+        response = chatbot(
+            user_id=user_id,
+            user_input=message
+        )
+    except LLMProviderError as exc:
+        raise HTTPException(
+            status_code=exc.status_code,
+            detail=exc.message
+        )
+
+    return {
+        "user_id": user_id,
+        "response": response
+    }