import streamlit as st
from datetime import datetime, timedelta
import os
import time
from openai import OpenAI
from dotenv import load_dotenv
load_dotenv()
# Initialize OpenAI client
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()
# Ensure dt is offset-naive if now is offset-naive
if date_obj.tzinfo is None and now.tzinfo is None:
pass # Both naive, do nothing
elif date_obj.tzinfo and now.tzinfo:
pass # Both aware, do nothing
else:
# Mix of naive and aware - remove tz info for simplicity
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"""
""", unsafe_allow_html=True)
time.sleep(duration)
placeholder.empty()
def show_error_message(message):
"""Display error message with animation"""
st.markdown(f"""
""", unsafe_allow_html=True)
def show_info_message(message):
"""Display info message with animation"""
st.markdown(f"""
""", 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:
# FALLBACK RESPONSE FOR DEMO (Handles Quota Exceeded / Network Issues)
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}"