Update app.py

app.py

@@ -10,6 +10,9 @@ import sqlite3
 from datetime import datetime, timedelta
 import re
 import os
+from streamlit_option_menu import option_menu
+import fitz  # PyMuPDF
+from bs4 import BeautifulSoup
 
 GROQ_API_KEY = st.secrets["GROQ_API_KEY"]
 RAPIDAPI_KEY = st.secrets["RAPIDAPI_KEY"]
@@ -313,9 +316,12 @@ def parse_duration(duration_str):
     except:
         return 0
 
+# -------------------------------
+# YouTube Data API Integration
+# -------------------------------
 
 @st.cache_data(ttl=86400)  # Cache results for 1 day
-def search_youtube_videos(query, max_results=3, video_duration="short", order="relevance"):
+def search_youtube_videos(query, max_results=3, video_duration="long", order="relevance"):
     """
     Searches YouTube for videos related to the query with additional filters.
 
@@ -363,6 +369,9 @@ def embed_youtube_videos(video_urls, topic):
         for url in video_urls:
             st.video(url)
 
+# -------------------------------
+# Database Functions
+# -------------------------------
 
 def init_db():
     """
@@ -469,6 +478,10 @@ 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")
 
@@ -765,7 +778,7 @@ def interview_preparation_module():
     st.header("Interview Preparation")
 
     st.write("""
-    Prepare for your interviews with tailored mock questions, expert tips, and curated video resources.
+    Prepare for your interviews with tailored mock questions and expert answers.
     """)
 
     # Create two columns for input fields
@@ -780,33 +793,34 @@ 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 to generate 50 interview questions with answers
             prompt = f"""
-            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.
+            Generate a list of 50 interview questions along with their answers for the position of {job_title} at {company}. Each question should be followed by a concise and professional answer.
             """
+
             try:
-                # 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}**.")
+                # Invoke the LLM to get questions and answers
+                qa_text = llm.invoke(prompt).content.strip()
+                # Split into question-answer pairs
+                qa_pairs = qa_text.split('\n\n')
+                st.subheader("Mock Interview Questions and Answers:")
+                for idx, qa in enumerate(qa_pairs, 1):
+                    if qa.strip():
+                        parts = qa.split('\n', 1)
+                        if len(parts) == 2:
+                            question = parts[0].strip()
+                            answer = parts[1].strip()
+                            st.markdown(f"**Q{idx}: {question}**")
+                            st.markdown(f"**A:** {answer}")
+                            st.write("---")
             except Exception as e:
-                st.error(f"Error generating interview topics: {e}")
+                st.error(f"Error generating interview questions: {e}")
 
 def personalized_learning_paths_module():
     st.header("Personalized Learning Paths")
 
     st.write("""
-    Receive tailored learning plans to help you acquire the skills needed for your desired career.
+    Receive tailored learning plans to help you acquire the skills needed for your desired career, complemented with curated video resources.
     """)
 
     # Create two columns for input fields
@@ -825,6 +839,27 @@ def personalized_learning_paths_module():
             if learning_path:
                 st.subheader("Your Personalized Learning Path:")
                 st.write(learning_path)
+                
+                # Assuming the learning path is divided into modules/subparts separated by newlines or numbering
+                # We'll extract subparts and embed YouTube videos for each
+                # Example format:
+                # 1. Module One
+                # 2. Module Two
+                # etc.
+
+                # Split learning path into modules
+                modules = re.split(r'\d+\.\s+', learning_path)
+                modules = [module.strip() for module in modules if module.strip()]
+                
+                st.subheader("Recommended YouTube Videos for Each Module:")
+                for module in modules:
+                    # Search for long videos related to the module
+                    video_urls = search_youtube_videos(query=module, max_results=2, video_duration="long")
+                    if video_urls:
+                        st.markdown(f"### {module}")
+                        embed_youtube_videos(video_urls, module)
+                    else:
+                        st.write(f"No videos found for **{module}**.")
             else:
                 st.error("Failed to generate learning path.")
 
@@ -1117,6 +1152,9 @@ 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
@@ -1135,13 +1173,23 @@ def main_app():
         unsafe_allow_html=True
     )
 
-    # Sidebar Navigation
+    # Sidebar Navigation using streamlit_option_menu
     with st.sidebar:
-        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"]
+        selected = option_menu(
+            menu_title="Main Menu",
+            options=["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,
+            styles={
+                "container": {"padding": "5!important", "background-color": "#2e7bcf"},
+                "icon": {"color": "white", "font-size": "18px"},
+                "nav-link": {"font-size": "16px", "text-align": "left", "margin": "0px", "--hover-color": "#6b9eff"},
+                "nav-link-selected": {"background-color": "#1e5aab"},
+            }
         )
 
     # Route to the selected page