Feature/issue 1087

config/recommendation_weights.json

@@ -0,0 +1,6 @@
+{
+  "skill": 3,
+  "level": 2,
+  "interest": 2,
+  "time": 1
+}

src/config.py

@@ -5,6 +5,8 @@
 # between environments (development, production, forks).
 
 import os
+import json
+from pathlib import Path
 
 class Config:
     """Base configuration class with sensible defaults."""
@@ -50,3 +52,160 @@ def get_og_image_url(cls):
     def get_base_url(cls):
         """Return the base URL without trailing slash."""
         return cls.BASE_URL.rstrip("/")
+    @classmethod
+    def load_recommendation_weights(cls):
+        """
+        Load recommendation scoring weights from config file.
+
+        The weights file is located at config/recommendation_weights.json
+        and can be overridden via RECOMMENDATION_WEIGHTS_PATH env var.
+
+        Returns:
+            dict: Weights dictionary with keys like "skill", "level", etc.
+
+        Raises:
+            FileNotFoundError: If the weights config file doesn't exist
+            ValueError: If the JSON is invalid or weights are malformed
+        """
+        weights_path = os.getenv(
+            "RECOMMENDATION_WEIGHTS_PATH",
+            Path(__file__).parent.parent / "config" / "recommendation_weights.json"
+        )
+
+        try:
+            with open(weights_path, 'r', encoding='utf-8') as f:
+                weights = json.load(f)
+
+            # Validate that all expected keys exist
+            expected_keys = {"skill", "level", "interest", "time"}
+            if not expected_keys.issubset(weights.keys()):
+                missing = expected_keys - set(weights.keys())
+                raise ValueError(f"Missing required weight keys: {missing}")
+
+            # Validate that all values are positive numbers
+            for key, value in weights.items():
+                if not isinstance(value, (int, float)) or value < 0:
+                    raise ValueError(f"Weight '{key}' must be a positive number, got {value}")
+
+            return weights
+
+        except FileNotFoundError:
+            raise FileNotFoundError(
+                f"Recommendation weights config not found at {weights_path}. "
+                "Please create config/recommendation_weights.json"
+            )
+        except json.JSONDecodeError as e:
+            raise ValueError(f"Invalid JSON in recommendation weights config: {e}")
+
+
+## Step 3: Add these lines at the end of the file (outside the Config class)
+
+# Lazy-load weights to avoid file I/O on import
+_RECOMMENDATION_WEIGHTS = None
+
+def get_recommendation_weights():
+    """
+    Get recommendation scoring weights (cached).
+
+    Weights are loaded once on first call and then cached.
+    """
+    global _RECOMMENDATION_WEIGHTS
+    if _RECOMMENDATION_WEIGHTS is None:
+        _RECOMMENDATION_WEIGHTS = Config.load_recommendation_weights()
+    return _RECOMMENDATION_WEIGHTS
+
+## Complete Updated config.py
+
+# config.py
+# Application configuration settings for DevPath.
+#
+# This module centralizes all configuration values that might change
+# between environments (development, production, forks).
+
+import os
+import json
+from pathlib import Path
+
+class Config:
+    """Base configuration class with sensible defaults."""
+
+    # Base URL for the application - used for OG tags and canonical URLs
+    # Can be overridden via environment variable for different deployments
+    BASE_URL = os.getenv("BASE_URL", "https://mydevpath-github.vercel.app")
+
+    # Application metadata for OG tags
+    SITE_NAME = "DevPath"
+    SITE_DESCRIPTION = "Get personalized coding project recommendations with step-by-step roadmaps and starter code."
+
+    # OG image path (relative to static folder)
+    OG_IMAGE_PATH = "/static/og-banner.png"
+
+    @classmethod
+    def get_og_image_url(cls):
+        """Return the full URL for the OG banner image."""
+        return f"{cls.BASE_URL.rstrip('/')}{cls.OG_IMAGE_PATH}"
+
+    @classmethod
+    def get_base_url(cls):
+        """Return the base URL without trailing slash."""
+        return cls.BASE_URL.rstrip("/")
+
+    @classmethod
+    def load_recommendation_weights(cls):
+        """
+        Load recommendation scoring weights from config file.
+
+        The weights file is located at config/recommendation_weights.json
+        and can be overridden via RECOMMENDATION_WEIGHTS_PATH env var.
+
+        Returns:
+            dict: Weights dictionary with keys like "skill", "level", etc.
+
+        Raises:
+            FileNotFoundError: If the weights config file doesn't exist
+            ValueError: If the JSON is invalid or weights are malformed
+        """
+        weights_path = os.getenv(
+            "RECOMMENDATION_WEIGHTS_PATH",
+            Path(__file__).parent.parent / "config" / "recommendation_weights.json"
+        )
+
+        try:
+            with open(weights_path, 'r', encoding='utf-8') as f:
+                weights = json.load(f)
+
+            # Validate that all expected keys exist
+            expected_keys = {"skill", "level", "interest", "time"}
+            if not expected_keys.issubset(weights.keys()):
+                missing = expected_keys - set(weights.keys())
+                raise ValueError(f"Missing required weight keys: {missing}")
+
+            # Validate that all values are positive numbers
+            for key, value in weights.items():
+                if not isinstance(value, (int, float)) or value < 0:
+                    raise ValueError(f"Weight '{key}' must be a positive number, got {value}")
+
+            return weights
+
+        except FileNotFoundError:
+            raise FileNotFoundError(
+                f"Recommendation weights config not found at {weights_path}. "
+                "Please create config/recommendation_weights.json"
+            )
+        except json.JSONDecodeError as e:
+            raise ValueError(f"Invalid JSON in recommendation weights config: {e}")
+
+
+# Lazy-load weights to avoid file I/O on import
+_RECOMMENDATION_WEIGHTS = None
+
+def get_recommendation_weights():
+    """
+    Get recommendation scoring weights (cached).
+
+    Weights are loaded once on first call and then cached.
+    """
+    global _RECOMMENDATION_WEIGHTS
+    if _RECOMMENDATION_WEIGHTS is None:
+        _RECOMMENDATION_WEIGHTS = Config.load_recommendation_weights()
+    return _RECOMMENDATION_WEIGHTS

src/static/script.js

@@ -939,12 +939,9 @@ async function updatePortfolioAnalysis() {
     clearAllErrors();
     hideSuggestions();
     resultsSection.style.display = "none";
-    showMoreBtn.style.display = "none";
-    allProjects = [];
-    visibleProjectCount = PROJECTS_PER_LOAD;
     if (typeof RecentlyViewed !== "undefined") {
     RecentlyViewed.clearHistory();
-    }    
+    }
     if (skillsInput) skillsInput.focus();
   }  
   var clearBtn = document.getElementById("clear-filters-btn");

src/templates/index.html

@@ -58,9 +58,6 @@
   {% include 'partials/theme_head.html' %}
   <link rel="stylesheet" href="/static/style.css" />
   <link rel="stylesheet" href="/static/bookmarks.css" />
-
-  <link rel="stylesheet" href="/static/animations.css" />
-
   <link rel="stylesheet" href="/static/recently-viewed.css" />
   <link
     href="https://fonts.googleapis.com/css2?family=Sora:wght@400;600;700;800&family=Inter:wght@400;500;600&display=swap"
@@ -1174,13 +1171,16 @@ <h3>No Projects Found</h3>
 
       <div class="results-grid" id="results-grid"></div>
 
-        <button
-          id="show-more-btn"
-          class="btn-submit"
-          style="display:none;"
-          type="button">
-          Show More Projects
-        </button>
+      <div class="recently-viewed-panel" id="recently-viewed-panel">
+        <div class="recently-viewed-header">
+          <div>
+            <h3>Recently Viewed</h3>
+            <p>Projects you've checked out recently.</p>
+          </div>
+          <span class="recently-viewed-count" id="recently-viewed-count">0 viewed</span>
+        </div>
+        <div class="recently-viewed-list" id="recently-viewed-list"></div>
+      </div>
 
       <div class="saved-projects-panel" id="saved-projects-panel">
         <div class="saved-projects-header">
@@ -1399,7 +1399,8 @@ <h4 class="footer-col-title">About Us</h4>
       });
     });
   </script>
-      <script src="https://unpkg.com/lenis@1.1.13/dist/lenis.min.js"></script>
+
+  <script src="https://unpkg.com/lenis@1.1.13/dist/lenis.min.js"></script>
 
   <script src="/static/animations.js"></script>
   <script src="/static/script.js"></script>

src/utils/recommender.py

@@ -10,6 +10,7 @@
 import os
 
 from utils.data_loader import load_all_projects
+# Import config - handle path for both direct imports and test imports
 
 MAX_RESULTS = 3
 MAX_RELATED = 3
@@ -35,27 +36,6 @@ def clear_caches():
 VALID_LEVELS = {"beginner", "intermediate", "advanced"}
 VALID_INTERESTS = {"web", "data", "education", "automation", "games", "cybersecurity", "devops", "backend", "tools", "productivity", "business logic", "mobile", "machine learning/ai", "artificial intelligence", "cloud computing"}
 VALID_TIME_AVAILABILITY = {"low", "medium", "high"}
-SCORING_WEIGHTS = {
-    "skill": 3,
-    "level": 2,
-    "interest": 2,
-    "time": 1,
-}
-
-SYNERGY_MAP = {
-    frozenset(["react", "node"]): 1.5,
-    frozenset(["react", "node.js"]): 1.5,
-    frozenset(["python", "django"]): 1.5,
-    frozenset(["python", "flask"]): 1.5,
-    frozenset(["html", "css", "javascript"]): 1.5,
-    frozenset(["vue", "node"]): 1.5,
-    frozenset(["angular", "node"]): 1.5,
-}
-
-WEIGHT_SKILL = SCORING_WEIGHTS["skill"]
-WEIGHT_LEVEL = SCORING_WEIGHTS["level"]
-WEIGHT_INTEREST = SCORING_WEIGHTS["interest"]
-WEIGHT_TIME = SCORING_WEIGHTS["time"]
 
 
 # Common aliases and abbreviations for skills
@@ -312,6 +292,15 @@ def score_single_project(project, user_skills, level, interest, time_availabilit
     if isinstance(interest, str):
         interest = [interest]
     TIME_RANKS = ["low", "medium", "high"]
+
+    import sys
+    import os
+    if os.path.dirname(os.path.dirname(os.path.abspath(__file__))) not in sys.path:
+        sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    from src.config import get_recommendation_weights
+
+    # Load weights from config
+    weights = get_recommendation_weights()
 
     user_time    = time_availability.strip().lower()
     project_time = project.get("time", "").strip().lower()
@@ -341,44 +330,24 @@ def score_single_project(project, user_skills, level, interest, time_availabilit
 
     skill_score = num_matched * weight_skill
     if project_skills:
-        coverage = num_matched / len(project_skills)
-        skill_score *= coverage
-
-    # Apply Synergy Multiplier
-    synergy_multiplier = 1.0
-    matched_set = set(matched_skills)
-    for synergy_group, multiplier in SYNERGY_MAP.items():
-        if synergy_group.issubset(matched_set):
-            synergy_multiplier = max(synergy_multiplier, multiplier)
-
-    score += skill_score * synergy_multiplier
+        coverage = matched_skills / len(project_skills)
+        score += matched_skills * weights["skill"] * coverage
+    else:
+        score += matched_skills * weights["skill"]
 
     level_match = False
     if project.get("level", "").lower() == level.lower():
-        score += weight_level
-        level_match = True
+        score += weights["level"]
 
     interest_match = False
     p_interest = project.get("interest", "").lower()
-    # Check if ANY of the user's multiple interests match the project interest
-    matched_interest = False
-    for u_interest in interest:
-        u_interest = u_interest.lower()
-        if p_interest == u_interest or (u_interest and u_interest in p_interest) or (p_interest and p_interest in u_interest):
-            matched_interest = True
-            break
-
-    if matched_interest:
-        score += weight_interest
-        interest_match = True
+    u_interest = interest.lower()
+    if p_interest == u_interest or (u_interest and u_interest in p_interest) or (p_interest and p_interest in u_interest):
+        score += weights["interest"]
 
     time_match = False
     if project.get("time", "").lower() == time_availability.lower():
-        score += weight_time
-        time_match = True
-
-    if graph is None:
-        graph = _load_skill_graph()
+        score += weights["time"]
 
     score += gap_boost(user_skills, project_skills, graph)