Update main.py

main.py

@@ -50,8 +50,6 @@ class SimpleRecommendation(BaseModel):
 
 class RecommendationResponse(BaseModel):
     recommendations: List[SimpleRecommendation]
-    page: int
-    total_pages: int
 
 class StatusResponse(BaseModel):
     status: str
@@ -178,68 +176,53 @@ def add_internship(internship: InternshipData, db_client: firestore.Client = Dep
     return {"status": "success", "internship_id": internship.id}
 
 @app.post("/profile-recommendations", response_model=RecommendationResponse)
-def get_profile_recommendations(profile: UserProfile, page: int = 1, page_size: int = 4):
+def get_profile_recommendations(profile: UserProfile):
     if chroma_collection is None or encoder is None:
         raise HTTPException(status_code=503, detail="Server is not ready.")
 
     query_text = f"Skills: {', '.join(profile.skills)}. Sectors: {', '.join(profile.sectors)}"
     query_embedding = encoder.encode([query_text])[0].tolist()
     
-    total_items = chroma_collection.count()
     results = chroma_collection.query(
         query_embeddings=[query_embedding], 
-        n_results=total_items
+        n_results=random.randint(3, 5) # Get 3 to 5 results
     )
     
+    recommendations = []
     ids = results.get('ids', [[]])[0]
     distances = results.get('distances', [[]])[0]
-    all_recommendations = [
-        {"internship_id": ids[i], "score": 1 - distances[i]} 
-        for i in range(len(ids))
-    ]
-
-    start_index = (page - 1) * page_size
-    end_index = start_index + page_size
-    paginated_results = all_recommendations[start_index:end_index]
     
-    total_pages = math.ceil(len(all_recommendations) / page_size)
-
-    return {
-        "recommendations": paginated_results,
-        "page": page,
-        "total_pages": total_pages
-    }
+    for i, internship_id in enumerate(ids):
+        recommendations.append({
+            "internship_id": internship_id,
+            "score": 1 - distances[i]
+        })
+        
+    return {"recommendations": recommendations}
 
 @app.post("/search", response_model=RecommendationResponse)
-def search_internships(search: SearchQuery, page: int = 1, page_size: int = 4):
+def search_internships(search: SearchQuery):
     if chroma_collection is None or encoder is None:
         raise HTTPException(status_code=503, detail="Server is not ready.")
-    
+        
     query_embedding = encoder.encode([search.query])[0].tolist()
-    total_items = chroma_collection.count()
+    
     results = chroma_collection.query(
         query_embeddings=[query_embedding], 
-        n_results=total_items
+        n_results=random.randint(3, 5) # Get 3 to 5 results
     )
     
+    recommendations = []
     ids = results.get('ids', [[]])[0]
     distances = results.get('distances', [[]])[0]
-    all_recommendations = [
-        {"internship_id": ids[i], "score": 1 - distances[i]} 
-        for i in range(len(ids))
-    ]
-    
-    start_index = (page - 1) * page_size
-    end_index = start_index + page_size
-    paginated_results = all_recommendations[start_index:end_index]
     
-    total_pages = math.ceil(len(all_recommendations) / page_size)
-
-    return {
-        "recommendations": paginated_results,
-        "page": page,
-        "total_pages": total_pages
-    }
+    for i, internship_id in enumerate(ids):
+        recommendations.append({
+            "internship_id": internship_id,
+            "score": 1 - distances[i]
+        })
+            
+    return {"recommendations": recommendations}
 
 # --------------------------------------------------------------------
 # ✅ NEW CHAT ENDPOINTS WITH MEMORY