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utils/utils/api_clients.py

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+import os
+from openai import OpenAI
+from pinecone import Pinecone
+
+
+def initialize_api_clients():
+    pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
+    pine_index = pc.Index("career-buddy-memo")
+    APIFY_TOKEN = os.environ.get("APIFY_TOKEN")
+    OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
+    TAVILY_API_KEY = os.getenv("TAVILY_API_KEY", "")
+    client = OpenAI(api_key=OPENAI_API_KEY)
+    
+    return pc, pine_index, APIFY_TOKEN, OPENAI_API_KEY, TAVILY_API_KEY, client
+
+
+
+def unload_model(model):
+    # This is a placeholder for actual model unloading logic
+    # In a real scenario, this would depend on the specific model and framework
+    print(f"[DEBUG] Unloading model: {model}")
+    del model
+
+

utils/utils/constants.py

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+TASK_DIFFICULTIES = ["Simple", "Moderate", "Challenging"]
+TASK_TAGS = ["Critical 🔴", "Important 🟠", "Optional 🟢"]
+reward_pool = ["Ice Cream 🍦", "Watch Party 🎬", "Spa Day 💆‍♀️"]
+task_data, claimed_rewards, available_rewards = [], [], []
+memo_data = []
+completed_tasks = set()
+completed_steps_box = set ()
+
+visual_steps = []
+last_reset = datetime.date.today()
+

utils/utils/data_helpers.py

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+import re
+import datetime
+import difflib
+from utils.constants import memo_data, task_data, completed_tasks
+
+def smart_label_converter(step_label):
+    if step_label.lower().startswith("milestone:"):
+        return "🎯 Build: " + step_label[len("milestone:"):].strip()
+    elif step_label.lower().startswith("action:"):
+        return "🔎 Research: " + step_label[len("action:"):].strip()
+    elif step_label.lower().startswith("resource:"):
+        return "🎓 Learn: " + step_label[len("resource:"):].strip()
+    else:
+        return step_label
+
+def extract_actions_from_feedback(feedback_text, source="AI"):
+    sentences = re.split(r'[.?!]\s+', feedback_text)
+    actions = []
+
+    for sentence in sentences:
+        lower = sentence.lower()
+        if any(kw in lower for kw in ["fix", "add", "update", "change", "optimize", "remove", "improve", "include", "enhance", "refactor"]):
+            cleaned = sentence.strip("•- ").strip().capitalize()
+            if cleaned:
+                actions.append({"type": "Action", "text": cleaned, "source": source})
+
+    return actions
+
+def render_memo():
+    if not memo_data:
+        return "📭 No roadmap data yet."
+
+    grouped = {"Milestone": [], "Resource": [], "Action": []}
+    for item in memo_data:
+        grouped[item["type"]].append(item)
+
+    output = ""
+    if grouped["Milestone"]:
+        output += "### 🎯 Milestones\n"
+        for m in grouped["Milestone"]:
+            output += f"- {m['text']}\n"
+
+    if grouped["Resource"]:
+        output += "\n### 🎓 Resources\n"
+        for r in grouped["Resource"]:
+            output += f"- {r['text']}\n"
+
+    if grouped["Action"]:
+        output += "\n### 🔎 Actions\n"
+        for a in grouped["Action"]:
+            output += f"- {a['text']}  \n  👉 [Add to Tasks]\n"
+
+    return output
+
+def convert_actions_to_tasks():
+    added = 0
+    for item in memo_data:
+        if item["type"] == "Action":
+            task_data.append({
+                "Task": item["text"],
+                "Duration": 1,
+                "Difficulty": "Simple",
+                "Tag": "Important 🟠",
+                "Points": 10
+            })
+            added += 1
+    # Assuming display_tasks() is a Gradio component update, it will be handled in app.py
+    return "Tasks converted!"
+
+course_suggestions = {
+        "data analyst": [
+            ("Google Data Analytics Professional Certificate", "https://www.coursera.org/professional-certificates/google-data-analytics"),
+            ("IBM Data Analyst Professional Certificate", "https://www.coursera.org/professional-certificates/ibm-data-analyst"),
+            ("Introduction to Data Analytics by IBM", "https://www.coursera.org/learn/introduction-to-data-analytics"),
+            ("Excel Basics for Data Analysis by IBM", "https://www.coursera.org/learn/excel-basics-data-analysis"),
+            ("Data Analysis using Excel and Tableau by EntryLevel", "https://www.entrylevel.net/post/beginner-data-analysis-courses-by-platform-with-certificates")
+        ],
+        "ux designer": [
+            ("Google UX Design Professional Certificate", "https://www.coursera.org/professional-certificates/google-ux-design"),
+            ("Introduction to UI and UX Design by Codecademy", "https://www.codecademy.com/learn/intro-to-ui-ux"),
+            ("UX Design Institute's Introduction to UX Design", "https://www.uxdesigninstitute.com/blog/best-free-ux-design-courses-in-2022/"),
+            ("Introduction to User Experience Design by Georgia Tech", "https://www.coursera.org/learn/user-experience-design"),
+            ("CareerFoundry UX Design Program", "https://careerfoundry.com/en/blog/ux-design/ux-design-course-online/")
+        ],
+        "software engineer": [
+            ("Introduction to Software Engineering by IBM", "https://www.coursera.org/learn/introduction-to-software-engineering"),
+            ("Python for Everybody Specialization by University of Michigan", "https://www.coursera.org/specializations/python"),
+            ("Full-Stack Engineer Career Path by Codecademy", "https://www.codecademy.com/learn/paths/full-stack-engineer-career-path"),
+            ("Software Engineering for Beginners by Udemy", "https://www.udemy.com/course/software-engineering-for-beginners/"),
+            ("Software Engineering Bootcamp by TripleTen", "https://tripleten.com/software-engineer/")
+        ],
+        "digital marketing": [
+            ("Fundamentals of Digital Marketing by Google Digital Garage", "https://learndigital.withgoogle.com/digitalgarage/course/digital-marketing"),
+            ("Digital Marketing Specialization by Coursera", "https://www.coursera.org/specializations/digital-marketing"),
+            ("The Complete Digital Marketing Course by Udemy", "https://www.udemy.com/course/learn-digital-marketing-course/"),
+            ("Digital Marketing Fundamentals by University of Edinburgh on edX", "https://www.edx.org/course/digital-marketing-fundamentals"),
+            ("Digital Marketing Course by CareerFoundry", "https://careerfoundry.com/en/blog/digital-marketing/online-digital-marketing-courses/")
+        ],
+        "project manager": [
+            ("Google Project Management Professional Certificate", "https://www.coursera.org/professional-certificates/google-project-management"),
+            ("Foundations of Project Management by Coursera", "https://www.coursera.org/learn/project-management-foundations"),
+            ("Project Management Basics by PMI", "https://www.pmi.org/learning/free-online-courses"),
+            ("Introduction to Project Management by University of Adelaide on edX", "https://www.edx.org/course/introduction-to-project-management"),
+            ("Project Management Principles and Practices Specialization by Coursera", "https://www.coursera.org/specializations/project-management")
+        ]
+    }
+
+def get_courses_for_goal(goal_key):
+    if goal_key not in course_suggestions:
+        match = difflib.get_close_matches(goal_key, course_suggestions.keys(), n=1, cutoff=0.6)
+        if match:
+            goal_key = match[0]
+    return course_suggestions.get(goal_key, [])
+
+class RoadmapUnlockManager:
+    def __init__(self):
+        self.weekly_steps = {}
+        self.current_week = "Week 1"
+        self.completed_tasks = set()
+
+    def load_steps(self, steps: list[str]):
+        self.weekly_steps = {}
+        current_label = None
+
+        for step in steps:
+            stripped = step.strip().strip("*")
+            if stripped.lower().startswith("week"):
+                current_label = stripped.split(":")[0].strip()
+                self.weekly_steps[current_label] = []
+            elif current_label:
+                self.weekly_steps[current_label].append(stripped)
+
+        self.current_week = list(self.weekly_steps.keys())[0] if self.weekly_steps else "Week 1"
+        self.completed_tasks.clear()
+
+    def get_current_choices(self):
+        return [
+            s for s in self.weekly_steps.get(self.current_week, [])
+            if not s.lower().startswith("week") and not s.startswith("**")
+        ]
+
+    def get_current_week_title(self):
+        return f"**📅 Current Focus: {self.current_week}**"
+
+    def update_completion(self, selected):
+        self.completed_tasks.update(selected)
+        all_current = set(self.get_current_choices())
+        if all_current.issubset(self.completed_tasks):
+            return self._unlock_next_week()
+        return f"✅ Progress: {len(self.completed_tasks)}/{len(all_current)}"
+
+    def _unlock_next_week(self):
+        weeks = list(self.weekly_steps.keys())
+        current_index = weeks.index(self.current_week)
+        if current_index + 1 < len(weeks):
+            self.current_week = weeks[current_index + 1]
+            self.completed_tasks.clear()
+            return f"🎉 All tasks done! Unlocked: {self.current_week}"
+        return "✅ All weeks completed!"
+
+def greet_user(uid, goal):
+    feedback = f"✅ Welcome back, **{uid}**!"
+    # Assuming recall_from_memory will be moved to memo.py or a separate data handling module
+    # For now, keep it as a placeholder or import if already moved
+    # recalled = recall_from_memory(uid, goal)
+    return feedback #, recalled
+
+def clean_text(text):
+    if not isinstance(text, str):
+        return ""
+    text = text.encode('utf-8', 'ignore').decode('utf-8', 'ignore')
+    text = re.sub(r'[^\x00-\x7F]+', '', text)
+    return text.strip()
+

utils/utils/embedding_model.py

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+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+def initialize_embedding_model():
+    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+

utils/utils/summarizer.py

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+from transformers import pipeline
+
+def initialize_summarizer():
+    return pipeline("summarization", model="facebook/bart-large-cnn")
+