Update app.py

app.py

@@ -1,19 +1,24 @@
+# app.py
+
 import streamlit as st
-from streamlit_option_menu import option_menu
-from langchain_groq import ChatGroq
-import fitz  # PyMuPDF
 import requests
-from bs4 import BeautifulSoup
+from langchain_groq import ChatGroq
+from streamlit_chat import message
 import plotly.express as px
-import re
 import pandas as pd
 import sqlite3
 from datetime import datetime, timedelta
-from streamlit_chat import message
+import re
 import os
 
+# -------------------------------
+# Initialize the LLM with Groq API Key
+# -------------------------------
+
+# Ensure API keys are stored securely using secrets.toml
 GROQ_API_KEY = st.secrets["GROQ_API_KEY"]
 RAPIDAPI_KEY = st.secrets["RAPIDAPI_KEY"]
+YOUTUBE_API_KEY = st.secrets["YOUTUBE_API_KEY"]
 
 llm = ChatGroq(
     temperature=0,
@@ -21,6 +26,10 @@ llm = ChatGroq(
     model_name="llama-3.1-70b-versatile"
 )
 
+# -------------------------------
+# Helper Functions with Caching
+# -------------------------------
+
 @st.cache_data(ttl=3600)
 def extract_text_from_pdf(pdf_file):
     """
@@ -313,9 +322,56 @@ def parse_duration(duration_str):
     except:
         return 0
 
-# -------------------------------
-# Database Functions
-# -------------------------------
+
+@st.cache_data(ttl=86400)  # Cache results for 1 day
+def search_youtube_videos(query, max_results=3, video_duration="short", order="relevance"):
+    """
+    Searches YouTube for videos related to the query with additional filters.
+
+    Args:
+        query (str): The search query/topic.
+        max_results (int): The maximum number of videos to retrieve.
+        video_duration (str): Duration filter ('any', 'short', 'medium', 'long').
+        order (str): Order of results ('date', 'rating', 'relevance', 'title', 'videoCount', 'viewCount').
+
+    Returns:
+        list: A list of YouTube video URLs.
+    """
+    api_key = YOUTUBE_API_KEY
+    search_url = "https://www.googleapis.com/youtube/v3/search"
+
+    params = {
+        "part": "snippet",
+        "q": query,
+        "type": "video",
+        "maxResults": max_results,
+        "videoDuration": video_duration,
+        "order": order,
+        "key": api_key
+    }
+
+    try:
+        response = requests.get(search_url, params=params)
+        response.raise_for_status()
+        results = response.json().get("items", [])
+        video_urls = [f"https://www.youtube.com/watch?v={item['id']['videoId']}" for item in results]
+        return video_urls
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error fetching YouTube videos: {e}")
+        return []
+
+def embed_youtube_videos(video_urls, topic):
+    """
+    Embeds YouTube videos under a specific topic within an expander.
+
+    Args:
+        video_urls (list): List of YouTube video URLs.
+        topic (str): The interview topic.
+    """
+    with st.expander(f"Videos for {topic}"):
+        for url in video_urls:
+            st.video(url)
+
 
 def init_db():
     """
@@ -422,10 +478,6 @@ def generate_learning_path(career_goal, current_skills):
         st.error(f"Error generating learning path: {e}")
         return ""
 
-# -------------------------------
-# Page Functions
-# -------------------------------
-
 def email_generator_page():
     st.header("Automated Email Generator")
 
@@ -722,7 +774,7 @@ def interview_preparation_module():
     st.header("Interview Preparation")
 
     st.write("""
-    Prepare for your interviews with tailored mock questions and expert tips.
+    Prepare for your interviews with tailored mock questions, expert tips, and curated video resources.
     """)
 
     # Create two columns for input fields
@@ -737,32 +789,27 @@ def interview_preparation_module():
             st.error("Please enter both job title and company name.")
             return
         with st.spinner("Generating questions..."):
+            # Example prompt to generate interview topics using LLM
             prompt = f"""
-            Generate a list of 10 interview questions for a {job_title} position at {company}. Include a mix of technical and behavioral questions.
+            Generate a list of 5 key topics that a candidate should prepare for an interview for the position of {job_title} at {company}. Each topic should be a concise phrase.
             """
             try:
-                questions = llm.invoke(prompt).content.strip()
-                st.subheader("Mock Interview Questions:")
-                st.write(questions)
-
-                # Optionally, provide sample answers or tips
-                if st.checkbox("Show Sample Answers"):
-                    sample_prompt = f"""
-                    Provide sample answers for the following interview questions for a {job_title} position at {company}.
-
-                    Questions:
-                    {questions}
-
-                    Sample Answers:
-                    """
-                    try:
-                        sample_answers = llm.invoke(sample_prompt).content.strip()
-                        st.subheader("Sample Answers:")
-                        st.write(sample_answers)
-                    except Exception as e:
-                        st.error(f"Error generating sample answers: {e}")
+                # Invoke the LLM to get topics
+                topics = llm.invoke(prompt).content.strip().split('\n')
+                topics = [topic.strip('- ').strip() for topic in topics if topic.strip()]
+                st.subheader("Key Interview Topics:")
+                for idx, topic in enumerate(topics, 1):
+                    st.markdown(f"{idx}. {topic}")
+
+                st.subheader("Recommended YouTube Videos:")
+                for topic in topics:
+                    video_urls = search_youtube_videos(query=topic, max_results=2)
+                    if video_urls:
+                        embed_youtube_videos(video_urls, topic)
+                    else:
+                        st.write(f"No videos found for **{topic}**.")
             except Exception as e:
-                st.error(f"Error generating interview questions: {e}")
+                st.error(f"Error generating interview topics: {e}")
 
 def personalized_learning_paths_module():
     st.header("Personalized Learning Paths")
@@ -1079,9 +1126,6 @@ def chatbot_support_page():
         else:
             message(chat['message'], is_user=False, avatar_style="bottts")
 
-# -------------------------------
-# Main App Function
-# -------------------------------
 
 def main_app():
     # Apply a consistent theme or style
@@ -1102,15 +1146,11 @@ def main_app():
 
     # Sidebar Navigation
     with st.sidebar:
-        selected = option_menu(
+        selected = st.selectbox(
             "Main Menu",
             ["Email Generator", "Cover Letter Generator", "Resume Analysis", "Application Tracking",
              "Interview Preparation", "Personalized Learning Paths", "Networking Opportunities",
-             "Salary Estimation", "Feedback", "Gamification", "Resource Library", "Success Stories", "Chatbot Support", "Help"],
-            icons=["envelope", "file-earmark-text", "file-person", "briefcase", "gear",
-                   "book", "people", "currency-dollar", "chat-left-text", "trophy", "collection", "star", "chat", "question-circle"],
-            menu_icon="cast",
-            default_index=0,
+             "Salary Estimation", "Feedback", "Gamification", "Resource Library", "Success Stories", "Chatbot Support", "Help"]
         )
 
     # Route to the selected page
@@ -1143,5 +1183,6 @@ def main_app():
     elif selected == "Help":
         help_page()
 
+
 if __name__ == "__main__":
     main_app()