Update app.py

app.py

@@ -1,54 +1,97 @@
 import streamlit as st
 import pandas as pd
-
-from utils import extract_text_from_pdf
-from extractor import extract_entities, extract_experience_years
-from recommender import (
-    score_skills,
-    classify_field,
-    recommend_countries,
-    recommend_certifications,
-    recommend_education,
-    generate_roadmap,
+import pdfplumber
+from extractor import (
+    extract_text_from_pdf,
+    extract_entities,
+    extract_experience_years,
+    classify_field
 )
 
-# Load data
+# Set page configuration
+st.set_page_config(page_title="Skill Scoring & Roadmap App", layout="wide")
+
+# Load datasets
 skills_df = pd.read_csv("data/skills_dataset.csv")
 countries_df = pd.read_csv("data/countries_dataset.csv")
 cert_df = pd.read_csv("data/certifications.csv")
 edu_tech_df = pd.read_csv("data/education_technical.csv")
 edu_non_tech_df = pd.read_csv("data/education_non_technical.csv")
-scholarship_df = pd.read_csv("data/scholarships.csv")  # Optional
+scholarship_df = pd.read_csv("data/scholarships.csv")
+
+# Helper function
+def score_skills(user_skills):
+    if not skills_df.shape[0]:
+        return 0
+    return int((len(user_skills) / len(skills_df)) * 100)
+
+def recommend_countries(skills, years_exp):
+    df = countries_df[countries_df['Skill'].isin(skills)]
+    df = df[df['MinExperience'] <= years_exp]
+    return df[["Country", "JobTitle", "AverageSalary", "VisaPath"]].drop_duplicates().reset_index(drop=True)
+
+def recommend_certifications(skills):
+    return cert_df[cert_df['Skill'].isin(skills)].reset_index(drop=True)
+
+def recommend_education(background):
+    return edu_tech_df.reset_index(drop=True) if background == "technical" else edu_non_tech_df.reset_index(drop=True)
 
-st.set_page_config(page_title="Skill Scoring App", layout="wide")
-st.title("📊 Personalized Skill Scoring & Career Roadmap App")
+def recommend_scholarships(field):
+    return scholarship_df[scholarship_df["Field"].str.lower() == field.lower()].reset_index(drop=True)
 
-uploaded_file = st.file_uploader("📤 Upload your CV (PDF)", type=["pdf"])
-age = st.number_input("Enter your age (if not in CV)", min_value=16, max_value=70)
+# UI
+st.title("📊 Personalized Skill Scoring & Global Career Roadmap")
+st.markdown("Upload your CV and receive a customized global career path, skill score, job matches, education and scholarship suggestions.")
+
+uploaded_file = st.file_uploader("📤 Upload your CV (PDF format)", type=["pdf"])
 
 if uploaded_file:
-    with st.spinner("Processing your CV..."):
+    with st.spinner("Analyzing your CV..."):
         text = extract_text_from_pdf(uploaded_file)
         skills, background = extract_entities(text, skills_df)
         years_exp = extract_experience_years(text)
-        score = score_skills(skills, skills_df)
-        field = classify_field(text, ["Engineering", "Medical", "IT", "Finance", "Labor", "HVAC"])
-        countries = recommend_countries(skills, countries_df)
-        certs = recommend_certifications(skills, cert_df)
-        edu = recommend_education(background, edu_tech_df, edu_non_tech_df)
-        roadmap = generate_roadmap(
-            {"years_experience": years_exp}, field, score, countries, certs, scholarship_df
-        )
+        field = classify_field(text)
+
+        score = score_skills(skills)
+        country_info = recommend_countries(skills, years_exp)
+        certs = recommend_certifications(skills)
+        edu = recommend_education(background)
+        scholarships = recommend_scholarships(field)
+
+    st.subheader("✅ Identified Skills")
+    st.write(skills or "No recognized skills found.")
+
+    st.subheader("📈 Skill Score")
+    st.metric("Your Skill Score", f"{score}/100")
+
+    st.subheader("🧠 Experience")
+    st.write(f"{years_exp} years of experience detected.")
+
+    st.subheader("📂 Categorized Field")
+    st.write(f"Detected Field: `{field}` | Background: `{background}`")
 
-    st.subheader("✅ Extracted Skills")
-    st.write(skills or "No skills found.")
+    st.subheader("🌍 Country Recommendations")
+    if not country_info.empty:
+        st.dataframe(country_info)
+    else:
+        st.write("No matching jobs found based on your current skills and experience.")
 
-    st.subheader("📈 Score & Field")
-    st.metric("Skill Score", f"{score}/100")
-    st.write(f"**Field Classification:** {field}")
+    st.subheader("🎓 Certifications (Free/Paid)")
+    if not certs.empty:
+        st.dataframe(certs)
+    else:
+        st.write("No certification recommendations found.")
 
-    st.subheader("🗺️ Roadmap")
-    st.markdown(roadmap, unsafe_allow_html=True)
+    st.subheader("🎓 Higher Education")
+    if not edu.empty:
+        st.dataframe(edu)
+    else:
+        st.write("No higher education recommendations.")
 
+    st.subheader("🎓 Scholarship Opportunities")
+    if not scholarships.empty:
+        st.dataframe(scholarships)
+    else:
+        st.write("No matching scholarships found.")
 else:
-    st.info("Please upload your CV to get started.")
+    st.info("Please upload your CV to begin.")