| import streamlit as st |
| from datetime import datetime, timedelta |
| import os |
| import time |
| from openai import OpenAI |
| from dotenv import load_dotenv |
|
|
| load_dotenv() |
|
|
| |
| client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) |
|
|
| def format_date(date_obj): |
| """Format datetime object to readable string""" |
| if isinstance(date_obj, str): |
| return date_obj |
| return date_obj.strftime("%B %d, %Y") |
|
|
| def time_ago(date_obj): |
| """Convert datetime to 'time ago' format""" |
| if not date_obj: |
| return "" |
| |
| now = datetime.now() |
| |
| if date_obj.tzinfo is None and now.tzinfo is None: |
| pass |
| elif date_obj.tzinfo and now.tzinfo: |
| pass |
| else: |
| |
| date_obj = date_obj.replace(tzinfo=None) |
| now = now.replace(tzinfo=None) |
| |
| diff = now - date_obj |
| |
| if diff.days > 365: |
| return f"{diff.days // 365} year{'s' if diff.days // 365 > 1 else ''} ago" |
| elif diff.days > 30: |
| return f"{diff.days // 30} month{'s' if diff.days // 30 > 1 else ''} ago" |
| elif diff.days > 0: |
| return f"{diff.days} day{'s' if diff.days > 1 else ''} ago" |
| elif diff.seconds > 3600: |
| return f"{diff.seconds // 3600} hour{'s' if diff.seconds // 3600 > 1 else ''} ago" |
| elif diff.seconds > 60: |
| return f"{diff.seconds // 60} minute{'s' if diff.seconds // 60 > 1 else ''} ago" |
| else: |
| return "Just now" |
|
|
| def validate_email(email): |
| """Validate email format""" |
| import re |
| pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$' |
| return re.match(pattern, email) is not None |
|
|
| def validate_phone(phone): |
| """Validate phone number format""" |
| import re |
| pattern = r'^[\d\s\+\-\(\)]{10,}$' |
| return re.match(pattern, phone) is not None |
|
|
| def show_success_message(message, duration=3): |
| """Display success message with animation""" |
| placeholder = st.empty() |
| placeholder.markdown(f""" |
| <div class="success-box"> |
| <h3>✅ Success!</h3> |
| <p>{message}</p> |
| </div> |
| """, unsafe_allow_html=True) |
| time.sleep(duration) |
| placeholder.empty() |
|
|
| def show_error_message(message): |
| """Display error message with animation""" |
| st.markdown(f""" |
| <div class="warning-box"> |
| <h3>⚠️ Error</h3> |
| <p>{message}</p> |
| </div> |
| """, unsafe_allow_html=True) |
|
|
| def show_info_message(message): |
| """Display info message with animation""" |
| st.markdown(f""" |
| <div class="info-box"> |
| <h3>ℹ️ Info</h3> |
| <p>{message}</p> |
| </div> |
| """, unsafe_allow_html=True) |
|
|
| def chatbot_response(user_message, context="women empowerment"): |
| """Get AI-powered chatbot response with Fallback for Demo Mode""" |
| try: |
| response = client.chat.completions.create( |
| model="gpt-3.5-turbo", |
| messages=[ |
| { |
| "role": "system", |
| "content": f"You are a helpful assistant for a women empowerment platform. Context: {context}" |
| }, |
| { |
| "role": "user", |
| "content": user_message |
| } |
| ], |
| max_tokens=300, |
| temperature=0.7 |
| ) |
| return response.choices[0].message.content |
| except Exception as e: |
| |
| error_str = str(e).lower() |
| if 'insufficient_quota' in error_str or '429' in error_str: |
| return ( |
| "💡 **Demo Mode Response:** \n\n" |
| "It seems the AI service is currently busy (Quota Exceeded). Here is some general advice:\n\n" |
| "**Health:** Focus on a balanced diet rich in iron (spinach, lentils) and calcium. Stay hydrated and aim for 30 mins of exercise daily.\n" |
| "**Career:** Upskill regularly, network with peers, and don't hesitate to negotiate for your worth.\n\n" |
| "*(Please check your OpenAI billing to enable real-time AI responses)*" |
| ) |
| return f"I'm having trouble connecting right now. Please try again later. Error: {str(e)}" |
|
|
| def generate_job_recommendation(user_skills, user_experience): |
| """Generate personalized job recommendations with Fallback""" |
| try: |
| prompt = f"Suggest 3 jobs for Skills: {user_skills}, Exp: {user_experience}" |
| response = client.chat.completions.create( |
| model="gpt-3.5-turbo", |
| messages=[{"role": "user", "content": prompt}], |
| max_tokens=200 |
| ) |
| return response.choices[0].message.content |
| except Exception: |
| return ( |
| "✨ **Recommended Roles (Demo):**\n" |
| "1. **Frontend Developer** - Matches your technical skills.\n" |
| "2. **Data Analyst** - Great for your analytical background.\n" |
| "3. **Product Manager** - Leverages your experience." |
| ) |
|
|
| def generate_course_recommendation(interests, current_level): |
| """Generate personalized course recommendations with Fallback""" |
| try: |
| prompt = f"Suggest 3 courses for Interests: {interests}, Level: {current_level}" |
| response = client.chat.completions.create( |
| model="gpt-3.5-turbo", |
| messages=[{"role": "user", "content": prompt}], |
| max_tokens=200 |
| ) |
| return response.choices[0].message.content |
| except Exception: |
| return ( |
| "📚 **Recommended Path (Demo):**\n" |
| "1. **Python for Beginners** (Udemy) - Great starting point.\n" |
| "2. **Google Data Analytics Cert** (Coursera) - Align with your interests.\n" |
| "3. **Intro to Web Dev** (freeCodeCamp)." |
| ) |
|
|
| def search_filter(items, query, search_fields): |
| """Filter items based on search query""" |
| if not query: |
| return items |
| |
| query = query.lower() |
| filtered = [] |
| |
| for item in items: |
| for field in search_fields: |
| if field in item and query in str(item[field]).lower(): |
| filtered.append(item) |
| break |
| |
| return filtered |
|
|
| def paginate(items, page=1, items_per_page=10): |
| """Paginate a list of items""" |
| start = (page - 1) * items_per_page |
| end = start + items_per_page |
| return items[start:end], len(items) |
|
|
| def get_sample_data_if_none(data, sample_data): |
| """Return sample data if database data is empty or None""" |
| if data and len(data) > 0: |
| return data |
| return sample_data |
|
|
| def format_currency(amount, currency="₹"): |
| """Format currency value""" |
| if amount is None: |
| return f"{currency}0" |
| return f"{currency}{amount:,.2f}" |
|
|
