Update App.py

App.py

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+#--------------- NEW Design CODE-------------------------#
+import streamlit as st
+import google.generativeai as genai
+import fitz
+import docx
+import pandas as pd
+from streamlit_autorefresh import st_autorefresh
+
+
+# --- PAGE CONFIG ---
+st.set_page_config(page_title="Tech Career Coach", layout="wide")
+
+# --- Wake Up Mode---- #
+st_autorefresh(interval=10 * 60 * 1000, limit=None, key="auto_refresh")
+# --- SIDEBAR NAVIGATION ---
+st.sidebar.title("🔍 Navigation")
+menu = st.sidebar.radio("Go to", ["Chatbot", "Resume Review", "Mentor Match", "Learning Path"])
+
+# --- GEMINI CONFIG ---
+api_key = st.secrets["GEMINI_API_KEY"]
+genai.configure(api_key=api_key)
+model = genai.GenerativeModel("gemini-1.5-flash")
+
+# --- SESSION STATE ---
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+# --- PROMPT FUNCTION ---
+def ask_single_prompt(prompt_text):
+    try:
+        response = model.generate_content(prompt_text)
+        return response.text
+    except Exception as e:
+        st.error(f"❌ Error: {str(e)}")
+        return None
+
+# --- CHATBOT PAGE ---
+if menu == "Chatbot":
+    st.title("💬 Tech Career Coach Chatbot")
+    st.caption("Ask about careers, mentorship, or learning paths.")
+
+    for msg in st.session_state["messages"]:
+        who = "🧑" if msg["role"] == "user" else "🤖"
+        st.markdown(f"{who}: **{msg['parts']}**")
+
+    user_input = st.chat_input("Type your question...")
+    if user_input:
+        st.session_state["messages"].append({"role": "user", "parts": user_input})
+        try:
+            response = model.generate_content(st.session_state["messages"])
+            st.session_state["messages"].append({"role": "model", "parts": response.text})
+            st.rerun()
+        except Exception as e:
+            st.error(f"❌ Error: {str(e)}")
+
+# --- RESUME REVIEW PAGE ---
+elif menu == "Resume Review":
+    st.title("📎 Resume Reviewer")
+    uploaded_file = st.file_uploader("Upload your resume (PDF, DOCX, or TXT)", type=["pdf", "docx", "txt"])
+
+    def extract_text_from_file(uploaded_file):
+        file_type = uploaded_file.name.split('.')[-1].lower()
+        if file_type == "txt":
+            return uploaded_file.read().decode("utf-8")
+        elif file_type == "pdf":
+            pdf = fitz.open(stream=uploaded_file.read(), filetype="pdf")
+            return "".join([page.get_text() for page in pdf])
+        elif file_type == "docx":
+            doc = docx.Document(uploaded_file)
+            return "\n".join([para.text for para in doc.paragraphs])
+        return "Unsupported file type."
+
+    if uploaded_file:
+        extracted_text = extract_text_from_file(uploaded_file)
+        st.markdown("**: Uploaded resume for review.**")
+        reply = ask_single_prompt(f"Please review this resume:{extracted_text}")
+        if reply:
+            st.markdown(f"🤖: {reply}")
+
+# --- MENTOR MATCH PAGE ---
+elif menu == "Mentor Match":
+    st.title("Mentor Match")
+    st.markdown("Find mentors based on your interests, preferences, and location.")
+
+    df = pd.read_csv("mentors.csv")
+
+    with st.container():
+        col1, col2, col3 = st.columns(3)
+        field = col1.selectbox("🎯 Field of interest", sorted(df["field"].unique()))
+        gender = col2.selectbox("🚻 Preferred gender", ["Any", "Female", "Male"])
+        location = col3.selectbox("📍 Preferred location", ["Any"] + sorted(df["location"].unique()))
+
+    # Filter logic
+    filtered = df[df["field"] == field]
+    if gender != "Any":
+        filtered = filtered[filtered["gender"] == gender]
+    if location != "Any":
+        filtered = filtered[filtered["location"] == location]
+
+    st.divider()
+    st.subheader("✨ Top Mentor Recommendations")
+
+    if not filtered.empty:
+        for _, mentor in filtered.head(3).iterrows():
+            # st.markdown(f"""
+            # <div style='padding: 1rem; border: 1px solid #DDD; border-radius: 10px; margin-bottom: 1rem; background-color: #f9f9f9;'>
+            #     <h4 style="margin-bottom: 0.3rem; color: black;">{mentor['name']}</h4>
+            #     <p style="margin: 0.2rem 0;">
+            #         <strong>📍 Location:</strong> {mentor['location']} &nbsp; | &nbsp;
+            #         <strong>💼 Field:</strong> {mentor['field']} &nbsp; | &nbsp;
+            #         <strong>🚻 Gender:</strong> {mentor['gender']}
+            #     </p>
+            #     <p style="margin: 0.2rem 0;">
+            #         <strong>📧 Email:</strong> <code>{mentor['email']}</code> &nbsp; | &nbsp;
+            #         <strong>🕓 Experience:</strong> {mentor['experience']} years
+            #     </p>
+            # </div>
+            # """, unsafe_allow_html=True)
+            st.markdown(f"""
+            <div style='padding: 1rem; border: 1px solid #DDD; border-radius: 10px; margin-bottom: 1rem; background-color: #f9f9f9; color: black;'>
+                <h4 style="margin-bottom: 0.3rem; color: inherit;">{mentor['name']}</h4>
+                <p style="margin: 0.2rem 0;">
+                    <strong>📍 Location:</strong> {mentor['location']} &nbsp; | &nbsp;
+                    <strong>💼 Field:</strong> {mentor['field']} &nbsp; | &nbsp;
+                    <strong>🚻 Gender:</strong> {mentor['gender']}
+            </p>
+            <p style="margin: 0.2rem 0;">
+                <strong>📧 Email:</strong> <code>{mentor['email']}</code> &nbsp; | &nbsp;
+                <strong>🕓 Experience:</strong> {mentor['experience']} years
+            </p>
+        </div>
+        """, unsafe_allow_html=True)
+    else:
+        st.warning("No mentors match your filters. Try adjusting the criteria.")
+
+
+# --- LEARNING PATH PAGE ---
+elif menu == "Learning Path":
+    st.title("📚 Personalized Learning Path")
+    domain = st.selectbox("Choose a career track:", [
+        "Frontend Development", "Data Science", "Cloud Engineering",
+        "Cybersecurity", "Product Management", "AI/ML",
+        "UX Design", "Backend Development", "Full Stack Development", "Data Engineering"
+    ])
+    if st.button("Suggest Learning Path"):
+        prompt = f"Suggest a beginner-to-intermediate learning path using freeCodeCamp or Coursera for someone interested in becoming a {domain}. Include key skills and timeline."
+        reply = ask_single_prompt(prompt)
+        if reply:
+            st.markdown(f"🤖: {reply}")