Create app.py

app.py

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+import streamlit as st
+import requests
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import warnings
+
+# Suppress specific warnings related to missing ScriptRunContext
+warnings.filterwarnings("ignore", message=".*missing ScriptRunContext.*")
+
+# 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 real API
+def get_scholarships(location, interests):
+    # Example: Using a mock API or replace it with a real API URL
+    # For now, we will use JSONPlaceholder for demonstration
+    url = "https://jsonplaceholder.typicode.com/posts"  # Mock API
+    response = requests.get(url)
+    
+    if response.status_code == 200:
+        # Sample response (You should replace this with real scholarship data)
+        return [{"title": f"Scholarship {i+1}", "description": "This is a sample description.", "eligibility": "Any student from any background."} for i in range(5)]
+    else:
+        return []
+
+# Function to get internships data from a real API
+def get_internships():
+    # Example: Using a mock API or replace it with a real API URL
+    url = "https://jsonplaceholder.typicode.com/posts"  # Mock API
+    response = requests.get(url)
+    
+    if response.status_code == 200:
+        # Sample response (You should replace this with real internship data)
+        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]
+
+# 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("---")
+
+# Your main logic here
+def main():
+    try:
+        # Your main code logic
+        pass
+    except Exception as e:
+        st.error(f"Error occurred: {e}")
+    finally:
+        # Cleanup actions if necessary
+        pass
+
+if __name__ == "__main__":
+    main()