Update app.py

app.py

@@ -1,60 +1,65 @@
 import streamlit as st
+import base64
+import os
+import tempfile
 from utils import (
-    detect_cv_field,
-    get_skills_suggestions,
-    get_certifications,
-    get_scholarships,
-    get_education_opportunities,
-    get_visa_opportunities,
+    parse_cv,
+    identify_field_with_llm,
+    generate_skill_score,
+    generate_llm_suggestions,
+    get_live_jobs,
+    generate_counselor_response,
+    generate_pdf_report
 )
 
-st.set_page_config(page_title="Universal Smart CV Analyzer", layout="wide")
+st.set_page_config(page_title="Smart CV Analyzer", layout="wide")
+st.title("📄 Universal Smart CV Analyzer & Career Roadmap")
 
-st.title("📄 Universal Smart CV Analyzer & Career Roadmap Generator")
-
-uploaded_file = st.file_uploader("Upload your CV (TXT or PDF recommended)", type=["txt", "pdf"])
+uploaded_file = st.file_uploader("Upload your CV (PDF format)", type=["pdf"])
 
 if uploaded_file:
-    file_text = uploaded_file.read()
-    try:
-        text = file_text.decode("utf-8")
-    except UnicodeDecodeError:
-        import PyPDF2
-        pdf_reader = PyPDF2.PdfReader(uploaded_file)
-        text = ""
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-
-    st.subheader("CV Summary")
-    st.write(text[:1000] + "...")
-
-    # --- Step 1: Identify field ---
-    field = detect_cv_field(text)
-    st.success(f"🧭 Detected Field: **{field.title()}**")
-
-    # --- Step 2: Personalized Suggestions ---
-    st.subheader("🔧 Recommended Skill Improvements")
-    skills = get_skills_suggestions(field)
-    st.write(skills)
-
-    st.subheader("📜 Relevant Certifications")
-    certs = get_certifications(field)
-    st.write(certs)
-
-    st.subheader("🎓 Scholarship Opportunities")
-    scholarships = get_scholarships(field)
-    st.write(scholarships)
-
-    st.subheader("🏫 Higher Education Opportunities")
-    education = get_education_opportunities(field)
-    st.write(education)
-
-    st.subheader("🌍 Suggested Visa Pathways")
-    visa_paths = get_visa_opportunities(field)
-    st.write(visa_paths)
-
-    # --- Final Counseling Advice ---
-    st.subheader("🧑‍💼 Career Counselor Advice")
-    st.markdown(f"""
-    Based on your background in **{field.title()}**, consider deepening your expertise in the top in-demand tools and seeking international opportunities through scholarships and visa-friendly countries. You may benefit from completing a few certifications like **{certs[0]}** and **{certs[1]}**. Keep your LinkedIn profile up to date and connect with professionals in this space.
-    """)
+    with st.spinner("Parsing CV..."):
+        with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+            tmp_file.write(uploaded_file.read())
+            tmp_path = tmp_file.name
+
+        raw_text = parse_cv(tmp_path)
+        os.unlink(tmp_path)
+
+        st.subheader("🧠 CV Extracted Text")
+        st.text_area("Extracted CV Content", raw_text, height=250)
+
+        with st.spinner("🔍 Identifying field/domain..."):
+            field = identify_field_with_llm(raw_text)
+            st.success(f"Detected Field: {field}")
+
+        with st.spinner("📊 Generating Skill Score..."):
+            score = generate_skill_score(raw_text)
+            st.metric("Skill Score (0–100)", score)
+
+        with st.spinner("🧠 Generating AI-based suggestions..."):
+            suggestions = generate_llm_suggestions(raw_text, field)
+
+        st.subheader("📌 Career Suggestions")
+        st.markdown(f"**Upskilling Skills:** {', '.join(suggestions['skills'])}")
+        st.markdown(f"**Certifications:** {', '.join(suggestions['certifications'])}")
+        st.markdown(f"**Scholarships:** {', '.join(suggestions['scholarships'])}")
+        st.markdown(f"**Education Opportunities:** {', '.join(suggestions['education'])}")
+        st.markdown(f"**Visa Pathways:** {', '.join(suggestions['visa'])}")
+
+        with st.spinner("🌍 Finding live jobs..."):
+            jobs = get_live_jobs(field)
+            st.subheader("💼 Live Job Opportunities")
+            for job in jobs:
+                st.markdown(f"- **[{job['title']}]({job['url']})** at *{job['company']}* ({job['location']})")
+
+        st.subheader("🧑‍🏫 Career Counselor Suggestion")
+        counselor_msg = generate_counselor_response(raw_text, field, score, suggestions)
+        st.info(counselor_msg)
+
+        # Downloadable report
+        st.subheader("📄 Download Your Personalized Career Report")
+        report_bytes = generate_pdf_report(raw_text, field, score, suggestions, jobs, counselor_msg)
+        b64 = base64.b64encode(report_bytes).decode()
+        href = f'<a href="data:application/pdf;base64,{b64}" download="Career_Roadmap_Report.pdf">📥 Download PDF Report</a>'
+        st.markdown(href, unsafe_allow_html=True)