Update app.py

app.py

@@ -1,130 +1,78 @@
 import streamlit as st
-from PyPDF2 import PdfReader
-from fpdf import FPDF
-import os
-
 from utils import (
-    extract_keywords,
-    identify_field,
-    is_technical_background,
-    calculate_cv_score,
-    suggest_upskilling,
-    suggest_certifications,
-    suggest_scholarships,
-    suggest_education_opportunities,
-    suggest_visa_opportunities,
-    get_job_listings
+    get_skills_suggestions,
+    get_certification_recommendations,
+    get_scholarship_suggestions,
+    get_education_opportunities,
+    get_visa_pathways,
+    get_job_recommendations,
+    get_cv_score,
+    get_field_classification,
+    get_personalized_advice,
 )
 
-st.set_page_config(page_title="Universal Smart CV Analyzer", layout="wide")
-st.title("📄 Universal Smart CV Analyzer & Career Roadmap")
-st.markdown("Upload your CV in PDF format to get a complete personalized analysis and roadmap.")
-
-# Upload PDF
-uploaded_file = st.file_uploader("Upload your CV", type="pdf")
-
-if uploaded_file:
-    with st.spinner("Reading and analyzing your CV..."):
-        pdf = PdfReader(uploaded_file)
-        text = ""
-        for page in pdf.pages:
-            text += page.extract_text() or ""
-
-        # Extract keywords
-        keywords = extract_keywords(text)
-        st.subheader("🔍 Extracted Keywords")
-        st.write(", ".join(keywords))
-
-        # Identify field
-        field = identify_field(keywords)
-        st.subheader("🧠 Predicted Field")
-        st.write(f"**{field}**")
-
-        # Score the CV
-        score = calculate_cv_score(text, keywords)
-        st.subheader("📊 CV Score")
-        st.metric(label="Skill Match Score", value=f"{score}/100")
-
-        # Determine technical background
-        background = is_technical_background(keywords)
-        st.subheader("🔧 Technical Background")
-        st.write(f"**{background}**")
-
-        # Suggestions Section
-        st.subheader("🚀 Suggested Upskilling")
-        upskills = suggest_upskilling(keywords)
-        st.write(upskills if upskills else "No suggestions found.")
+st.set_page_config(page_title="Smart CV Analyzer & Career Roadmap", layout="wide")
 
-        st.subheader("🎓 Certifications")
-        certifications = suggest_certifications(keywords)
-        st.write(certifications if certifications else "No certifications found.")
+st.title("📄 Universal Smart CV Analyzer & 🌍 Career Roadmap Generator")
+st.markdown("Upload your CV and get a detailed analysis including personalized job suggestions, skill-building advice, education and visa guidance, and a tailored career roadmap.")
 
-        st.subheader("💸 Scholarships")
-        scholarships = suggest_scholarships(keywords)
-        st.write(scholarships if scholarships else "No scholarships found.")
+uploaded_file = st.file_uploader("Upload your CV (.txt or .pdf)", type=["txt", "pdf"])
 
-        st.subheader("🏫 Education Opportunities")
-        education = suggest_education_opportunities(keywords)
-        st.write(education if education else "No educational programs found.")
-
-        st.subheader("🌍 Visa Opportunities")
-        visas = suggest_visa_opportunities(keywords)
-        st.write(visas if visas else "No visa opportunities found.")
-
-        st.subheader("💼 Job Listings")
-        job_df = get_job_listings(keywords, location="Pakistan")
-        if not job_df.empty:
-            st.dataframe(job_df)
-        else:
-            st.write("No jobs found.")
-
-        # PDF Report Generator
-        st.subheader("📥 Generate PDF Report")
-
-        class PDF(FPDF):
-            def chapter_title(self, title):
-                self.set_font("Arial", "B", 12)
-                self.set_fill_color(220, 220, 220)
-                self.cell(0, 10, title, ln=True, fill=True)
-
-            def chapter_body(self, body):
-                self.set_font("Arial", "", 11)
-                self.multi_cell(0, 10, body)
-                self.ln()
-
-        if st.button("Generate & Download Report"):
-            with st.spinner("Generating PDF report..."):
-                pdf = PDF()
-                pdf.add_page()
-                pdf.set_title("CV Analysis Report")
-                pdf.chapter_title("📄 CV Analysis Report")
-                pdf.chapter_title("Predicted Field:")
-                pdf.chapter_body(field)
-                pdf.chapter_title("Skill Match Score:")
-                pdf.chapter_body(f"{score}/100")
-                pdf.chapter_title("Technical Background:")
-                pdf.chapter_body(background)
-                pdf.chapter_title("Extracted Keywords:")
-                pdf.chapter_body(", ".join(keywords))
-                pdf.chapter_title("Suggested Upskilling:")
-                pdf.chapter_body(", ".join(upskills))
-                pdf.chapter_title("Certifications:")
-                pdf.chapter_body(", ".join(certifications))
-                pdf.chapter_title("Scholarships:")
-                pdf.chapter_body(", ".join(scholarships))
-                pdf.chapter_title("Education Opportunities:")
-                pdf.chapter_body(", ".join(education))
-                pdf.chapter_title("Visa Opportunities:")
-                pdf.chapter_body(", ".join(visas))
-
-                output_path = "cv_analysis_report.pdf"
-                pdf.output(output_path)
-
-                with open(output_path, "rb") as f:
-                    base64_pdf = f.read()
-                st.download_button(
-                    label="📄 Download CV Report",
-                    data=base64_pdf,
-                    file_name="cv_analysis_report.pdf",
-                    mime="application/pdf",
-                )
+if uploaded_file:
+    file_ext = uploaded_file.name.split('.')[-1]
+    if file_ext == "txt":
+        cv_text = uploaded_file.read().decode("utf-8")
+    elif file_ext == "pdf":
+        import PyPDF2
+        reader = PyPDF2.PdfReader(uploaded_file)
+        cv_text = "\n".join(page.extract_text() for page in reader.pages if page.extract_text())
+    else:
+        st.error("Unsupported file format.")
+        st.stop()
+
+    with st.spinner("Analyzing your CV..."):
+        score = get_cv_score(cv_text)
+        field = get_field_classification(cv_text)
+        skills = get_skills_suggestions(cv_text)
+        certs = get_certification_recommendations(cv_text)
+        scholarships = get_scholarship_suggestions(cv_text)
+        education = get_education_opportunities(cv_text)
+        visa = get_visa_pathways(cv_text, country="USA")
+        jobs = get_job_recommendations(cv_text, location="USA")
+        advice = get_personalized_advice(cv_text)
+
+    st.header("📊 CV Summary")
+    st.write(f"**Score:** {score}/100")
+    st.write(f"**Field/Domain:** {field}")
+
+    st.header("💡 Personalized Career Advice")
+    st.success(advice)
+
+    st.header("🧠 Recommended Skills to Learn")
+    for skill in skills:
+        st.markdown(f"- {skill}")
+
+    st.header("🎓 Recommended Certifications")
+    for cert in certs:
+        st.markdown(f"- {cert}")
+
+    st.header("🎯 Scholarship Opportunities")
+    for s in scholarships:
+        st.markdown(f"- {s}")
+
+    st.header("🏫 Education Paths")
+    for edu in education:
+        st.markdown(f"- {edu}")
+
+    st.header("🛂 Visa Pathways")
+    for v in visa:
+        st.markdown(f"- {v}")
+
+    st.header("💼 Job Opportunities (USA)")
+    for job in jobs:
+        st.subheader(job.get("title", "N/A"))
+        st.markdown(f"**Company:** {job.get('company', 'Unknown')}")
+        st.markdown(f"**Location:** {job.get('location', 'Unknown')}")
+        st.markdown(f"**Description:** {job.get('description', '')[:300]}...")
+        st.markdown(f"[Apply Here]({job.get('url', '#')})")
+        st.markdown("---")