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6b98981 dfe09a2 6b98981 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | import pandas as pd
import networkx as nx
from collections import Counter
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import io
import base64
class RoutingEngine:
def __init__(self, data_path):
self.df = pd.read_csv(data_path)
self.df.columns = self.df.columns.str.strip()
self.G = nx.Graph()
self.all_unique_skills = set()
self.all_jobs = set()
self._build_graph()
def _build_graph(self):
self.df['All_Skills'] = (
self.df['Skills'].fillna('') + "," +
self.df.get('Programming Languages', pd.Series([''] * len(self.df))) + "," +
self.df['Tools'].fillna('')
)
# Split skills and explode
df_exploded = self.df.assign(All_Skills=self.df['All_Skills'].str.split(',')).explode('All_Skills')
df_exploded['All_Skills'] = df_exploded['All_Skills'].str.strip()
# Remove empty strings
df_exploded = df_exploded[df_exploded['All_Skills'] != '']
for _, row in df_exploded.iterrows():
job = row['Job roles']
skill = row['All_Skills']
if isinstance(skill, str) and len(skill) > 1:
self.G.add_edge(job, skill)
self.all_unique_skills.add(skill)
self.all_jobs.add(job)
def get_skill_list(self):
return sorted([str(s) for s in self.all_unique_skills if isinstance(s, str)])
def get_job_list(self):
return sorted(list(self.all_jobs))
def get_gap(self, target_job, user_skills):
if target_job not in self.G.nodes:
return []
required_skills = set(self.G.neighbors(target_job))
current_skills = set(user_skills)
missing_skills = list(required_skills - current_skills)
return missing_skills
def recommend(self, user_skills):
possible_jobs = []
for skill in user_skills:
if skill in self.G.nodes:
neighbors = list(self.G.neighbors(skill))
possible_jobs.extend(neighbors)
if not possible_jobs:
return None, []
top_jobs_counter = Counter(possible_jobs).most_common(1)
best_job = top_jobs_counter[0][0]
missing_skills = self.get_gap(best_job, user_skills)
return best_job, missing_skills
def get_career_transition_path(self, current_job, target_job):
"""Finds the shortest upskilling path between two roles via shared skills."""
if current_job not in self.G.nodes or target_job not in self.G.nodes:
return None
try:
# NetworkX finds the shortest path alternating: Job -> Skill -> Job
path = nx.shortest_path(self.G, source=current_job, target=target_job)
skills_to_learn = [node for node in path if node in self.all_unique_skills]
stepping_stones = [node for node in path if node in self.all_jobs and node not in (current_job, target_job)]
return {
"path": path,
"skills_to_learn": skills_to_learn,
"stepping_stones": stepping_stones
}
except Exception:
return None
def get_subgraph_figure_base64(self, center_node, user_skills, depth=1):
try:
if center_node not in self.G.nodes: return None
skills = list(self.G.neighbors(center_node))
subG = self.G.subgraph([center_node] + skills)
plt.figure(figsize=(8, 6), facecolor='#0f172a')
pos = nx.spring_layout(subG, seed=42, k=0.5)
nx.draw_networkx_edges(subG, pos, edge_color='#334155', alpha=0.5)
node_colors = []
for n in subG.nodes():
if n == center_node:
node_colors.append('#ffffff')
elif n in user_skills:
node_colors.append('#047857')
else:
node_colors.append('#dc2626')
nx.draw_networkx_nodes(subG, pos, node_color=node_colors, node_size=600, alpha=0.9)
labels = {n: n for n in subG.nodes()}
nx.draw_networkx_labels(subG, pos, labels=labels, font_size=8, font_color='white', font_family='sans-serif')
plt.title(f"Skill Gap Map: {center_node}", color='white')
plt.axis('off')
buf = io.BytesIO()
plt.savefig(buf, format='png', bbox_inches='tight', facecolor='#0f172a')
plt.close()
buf.seek(0)
img_base64 = base64.b64encode(buf.read()).decode('utf-8')
return f"data:image/png;base64,{img_base64}"
except Exception as e:
print(f"Graph Error: {e}")
return None
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