Update utils/mistral.py

utils/mistral.py

@@ -370,6 +370,15 @@ def extract_link_details(text):
     links_RE = [link for link in links_RE if not any(email in link for email in emails)]
     
     return links_RE 
+
+# For handling multiple data 
+def normalize_data(value):
+    """Replace empty lists with None and convert strings to lists."""
+    if value == []:
+        return None
+    elif isinstance(value, str):
+        return [value]
+    return value
     
 def process_resume_data(file_path):
     resume_text, hyperlinks = extract_text_based_on_format(file_path)
@@ -410,33 +419,33 @@ def process_resume_data(file_path):
         # Combine both personal and professional details into a structured output
         result = {
             "personal": {
-                "name": per_data.get('personal', {}).get('name', None),
-                "contact": per_data.get('personal', {}).get('contact_number', None),
-                "email": per_data.get('personal', {}).get('email', None),
-                "location": per_data.get('personal', {}).get('address', None),
-                "linkedin": linkedin_links,
-                "github": github_links,
-                "other_links": hyperlinks  # Store remaining links if needed
+                "name": normalize_data(per_data.get('personal', {}).get('name', None)),
+                "contact": normalize_data(per_data.get('personal', {}).get('contact_number', None)),
+                "email": normalize_data(per_data.get('personal', {}).get('email', None)),
+                "location": normalize_data(per_data.get('personal', {}).get('address', None)),
+                "linkedin": normalize_data(linkedin_links),
+                "github": normalize_data(github_links),
+                "other_links": normalize_data(hyperlinks)
             },
             "professional": {
-                "technical_skills": pro_data.get('professional', {}).get('technical_skills', None),
-                "non_technical_skills": pro_data.get('professional', {}).get('non_technical_skills', None),
-                "tools": pro_data.get('professional', {}).get('tools', None),
+                "technical_skills": normalize_data(pro_data.get('professional', {}).get('technical_skills', None)),
+                "non_technical_skills": normalize_data(pro_data.get('professional', {}).get('non_technical_skills', None)),
+                "tools": normalize_data(pro_data.get('professional', {}).get('tools', None)),
                 "experience": [
                     {
-                        "company": pro_data.get('professional', {}).get('companies_worked_at', None),
-                        "projects": pro_data.get('professional', {}).get('projects', None),
-                        "role": pro_data.get('professional', {}).get('roles', None),
-                        "years": pro_data.get('professional', {}).get('experience', None),
-                        "project_experience": pro_data.get('professional', {}).get('projects_experience', None)
+                        "company": normalize_data(pro_data.get('professional', {}).get('companies_worked_at', None)),
+                        "projects": normalize_data(pro_data.get('professional', {}).get('projects', None)),
+                        "role": normalize_data(pro_data.get('professional', {}).get('roles', None)),
+                        "years": normalize_data(pro_data.get('professional', {}).get('experience', None)),
+                        "project_experience": normalize_data(pro_data.get('professional', {}).get('projects_experience', None))
                     }
                 ],
                 "education": [
                     {
-                        "qualification": Edu_data.get('educational', {}).get('qualifications', None),
-                        "university": Edu_data.get('educational', {}).get('university', None),
-                        "course": Edu_data.get('educational', {}).get('courses', None),
-                        "certificate": Edu_data.get('educational', {}).get('certifications', None)
+                        "qualification": normalize_data(Edu_data.get('educational', {}).get('qualifications', None)),
+                        "university": normalize_data(Edu_data.get('educational', {}).get('university', None)),
+                        "course": normalize_data(Edu_data.get('educational', {}).get('courses', None)),
+                        "certificate": normalize_data(Edu_data.get('educational', {}).get('certifications', None))
                     }
                 ]
             }