Update utils/suggestions.py

utils/suggestions.py

@@ -1,48 +1,68 @@
-import pandas as pd
 import os
+import pandas as pd
 
-BASE_DIR = os.path.join(os.path.dirname(__file__), "..", "csvs")
+CSV_FOLDER = os.path.join(os.path.dirname(__file__), '../csvs')
 
 def load_csv(filename):
-    path = os.path.join(BASE_DIR, filename)
+    path = os.path.join(CSV_FOLDER, filename)
     return pd.read_csv(path)
 
+def extract_field_from_text(text):
+    # Dummy implementation: extract the main field keyword from the parsed CV text
+    # You should replace this with your actual field extraction logic or pass field directly
+    keywords = [
+        "accounting", "agriculture", "architecture", "artificial intelligence", "biology", 
+        "business analysis", "chemistry", "civil engineering", "data science", "dentistry",
+        "electrical engineering", "finance", "graphic design", "healthcare", "hospitality",
+        "information technology", "journalism", "law", "marketing", "mechanical engineering",
+        "nursing", "pharmacy", "psychology", "quality assurance", "robotics", "sales",
+        "software development", "statistics", "teaching", "ux design", "veterinary",
+        "web development", "x-ray technology", "youth counseling", "zoology"
+    ]
+    text_lower = text.lower()
+    for kw in keywords:
+        if kw in text_lower:
+            return kw
+    return "general"  # fallback
+
 def match_field(field, df):
-    field = field.strip().lower()
-    match = df[df['field'].str.lower().str.contains(field)]
-    if not match.empty:
-        return match['suggestion'].tolist()
-    return ["No specific suggestions found for this field."]
+    field_lower = field.lower()
+    # Try exact match first
+    matched = df[df['field'].str.lower() == field_lower]
+    if matched.empty:
+        # Try contains match for partial matches
+        matched = df[df['field'].str.lower().str.contains(field_lower)]
+    if matched.empty:
+        # fallback: return empty dataframe
+        return pd.DataFrame()
+    return matched
 
-def get_certification_suggestions(field):
+def get_certification_suggestions(parsed_text):
+    field = extract_field_from_text(parsed_text)
     df = load_csv("certifications.csv")
-    return match_field(field, df)
+    matched = match_field(field, df)
+    return matched['suggestion'].tolist()
 
-def get_higher_education_suggestions(field):
-    df = load_csv("education.csv")
-    return match_field(field, df)
+def get_higher_education_suggestions(parsed_text):
+    field = extract_field_from_text(parsed_text)
+    df = load_csv("higher_education.csv")
+    matched = match_field(field, df)
+    return matched['suggestion'].tolist()
 
-def get_visa_recommendations(field):
-    df = load_csv("visa.csv")
-    return match_field(field, df)
+def get_visa_recommendations(parsed_text):
+    field = extract_field_from_text(parsed_text)
+    df = load_csv("visa_recommendations.csv")
+    matched = match_field(field, df)
+    return matched['suggestion'].tolist()
 
-def get_career_advice(field):
+def get_career_advice(parsed_text):
+    field = extract_field_from_text(parsed_text)
     df = load_csv("career_advice.csv")
-    return match_field(field, df)
+    matched = match_field(field, df)
+    return matched['suggestion'].tolist()
 
 def get_job_listings(parsed_text):
-    keywords = extract_keywords(parsed_text)
-    jobs_df = pd.read_csv("data/job_listings.csv")
-    
-    matched_jobs = []
-
-    for _, row in jobs_df.iterrows():
-        job_keywords = row["keywords"].split(",")
-        if any(kw.lower() in [k.lower() for k in keywords] for kw in job_keywords):
-            matched_jobs.append(f"{row['title']} at {row['company']} ({row['location']})")
-
-    if not matched_jobs:
-        return ["No job listings found for your field."]
-    
-    return matched_jobs
-
+    field = extract_field_from_text(parsed_text)
+    df = load_csv("job_listings.csv")
+    matched = match_field(field, df)
+    return matched['listing'].tolist()