app.py

import streamlit as st
import requests
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from transformers import MarianMTModel, MarianTokenizer
import sentencepiece

# Set up the Streamlit page
st.title("AI Opportunity Finder for Youth")
st.write("Find Scholarships, Internships, Online Courses, and more!")

# Function to get scholarships data from a mock API
def get_scholarships(location, interests):
    url = "https://jsonplaceholder.typicode.com/posts"  # Mock API for testing
    response = requests.get(url)
    
    if response.status_code == 200:
        # Return a list of mock scholarships
        return [{"title": f"Scholarship {i+1}", "description": post['body'], "eligibility": "Any student from any background."} for i, post in enumerate(response.json())[:5]]
    else:
        return []

# Function to get internships data from a mock API
def get_internships():
    url = "https://jsonplaceholder.typicode.com/posts"  # Mock API for testing
    response = requests.get(url)
    
    if response.status_code == 200:
        # Return a list of mock internships
        return [{"jobtitle": f"Internship {i+1}", "company": "Sample Company", "location": "Remote", "snippet": "Description of the internship."} for i in range(5)]
    else:
        return []

# Function to recommend opportunities based on user input
def recommend_opportunities(user_interests, user_skills, opportunities):
    # Combine user profile into a single string
    user_profile = [f"{user_interests} {user_skills}"]
    
    # Create text data for opportunities based on description & eligibility
    opportunities_text = [f"{opportunity['description']} {opportunity['eligibility']}" for opportunity in opportunities]
    
    # Vectorize the text using TF-IDF
    vectorizer = TfidfVectorizer(stop_words='english')
    tfidf_matrix = vectorizer.fit_transform(opportunities_text + user_profile)
    
    # Compute cosine similarity
    cosine_sim = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1])
    
    # Get the indices of the top 5 recommended opportunities
    recommendations = cosine_sim[0].argsort()[-5:][::-1]
    
    # Return recommended opportunities
    return [opportunities[i] for i in recommendations]

# Function to load MarianMT translation model
def load_translation_model(target_language):
    model_name = f'Helsinki-NLP/opus-mt-en-{target_language}'
    tokenizer = MarianTokenizer.from_pretrained(model_name)
    model = MarianMTModel.from_pretrained(model_name)
    return model, tokenizer

# Function to translate text using MarianMT
def translate_text(text, target_language):
    try:
        model, tokenizer = load_translation_model(target_language)
        
        # Tokenize and translate text
        tokens = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
        translated = model.generate(**tokens)
        translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
        
        return translated_text
    except Exception as e:
        return f"Error during translation: {str(e)}"

# User input for profile
st.sidebar.header("User Profile")
location = st.sidebar.text_input("Location", "Pakistan")  # Default to 'Pakistan'
skills = st.sidebar.text_input("Skills (e.g., Python, Marketing)")
interests = st.sidebar.text_input("Interests (e.g., Technology, Science)")

# Fetch scholarships based on user input
scholarships = get_scholarships(location, interests)

# Display scholarships if available
if scholarships:
    st.write("Scholarships found:")
    for scholarship in scholarships:
        st.write(f"Title: {scholarship['title']}")
        st.write(f"Description: {scholarship['description']}")
        st.write(f"Eligibility: {scholarship['eligibility']}")
        st.write("---")
else:
    st.write("No scholarships found based on your criteria.")

# Fetch internships based on user input
internships = get_internships()

# Display internships if available
if internships:
    st.write("Internships found:")
    for internship in internships:
        st.write(f"Title: {internship['jobtitle']}")
        st.write(f"Company: {internship['company']}")
        st.write(f"Location: {internship['location']}")
        st.write(f"Snippet: {internship['snippet']}")
        st.write("---")
else:
    st.write("No internships found.")

# AI-based recommendations for opportunities
if st.sidebar.button("Get AI Recommendations"):
    # Combine scholarships and internships for recommendations
    all_opportunities = scholarships + internships
    
    # Get AI recommendations based on user input
    recommended_opportunities = recommend_opportunities(interests, skills, all_opportunities)
    
    # Display recommended opportunities
    st.write("Recommended Opportunities based on your profile:")
    for opportunity in recommended_opportunities:
        st.write(f"Title: {opportunity['title']}")
        st.write(f"Description: {opportunity['description']}")
        st.write(f"Eligibility: {opportunity.get('eligibility', 'Not available')}")
        st.write("---")

# Language selection
languages = {
    'English': 'english',
    'German': 'deutch',
    'French': 'french',
    'Spanish': 'spanish',
    'Italian': 'italian',
    'Portuguese': 'portugese',
    'Chinese': 'chinese',
    'Arabic': 'arabic',
    'Russian': 'russian',
    'Japanese': 'japanese',
    'Korean': 'korean',
    'Urdu': 'urdu'
}

# Dropdown for language selection
selected_language = st.selectbox("Select Language", list(languages.keys()))

# Translate the opportunity description based on the selected language
if selected_language != 'English':
    # Translate the title of the app or a sample text
    translated_text = translate_text("Hello, welcome to AI Opportunity Finder!", languages[selected_language])
    st.write(f"Translated Text ({selected_language}): {translated_text}")