Update career_data.py

career_data.py

@@ -1,24 +1,50 @@
-def get_career_recommendations(skills: str, interests: str):
-    # Mock data: mapping skills and interests to careers
-    career_options = {
-        "programming": "Software Engineer",
-        "design": "Graphic Designer",
-        "management": "Project Manager",
-        "communication": "Marketing Specialist",
-    }
+from transformers import AutoModel, AutoTokenizer
+import torch
 
-    # Simple rule-based matching
-    skills = skills.lower().split(",")
-    interests = interests.lower().split(",")
-    recommendations = []
+# Load model and tokenizer
+model_name = "minishlab/M2V_base_output"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+# Career options with precomputed skills and interests
+career_options = {
+    "Software Engineer": {
+        "skills": "programming, problem-solving",
+        "interests": "technology, innovation"
+    },
+    "Graphic Designer": {
+        "skills": "design, creativity",
+        "interests": "art, visual communication"
+    },
+    "Project Manager": {
+        "skills": "management, organization",
+        "interests": "leadership, strategy"
+    },
+    # Add more careers as needed
+}
 
-    for skill in skills:
-        if skill.strip() in career_options:
-            recommendations.append(career_options[skill.strip()])
+# Generate embeddings for career options
+def get_embedding(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        embedding = model(**inputs).last_hidden_state.mean(dim=1).squeeze()
+    return embedding
 
-    for interest in interests:
-        if interest.strip() in career_options and career_options[interest.strip()] not in recommendations:
-            recommendations.append(career_options[interest.strip()])
+career_embeddings = {}
+for career, attributes in career_options.items():
+    combined_text = attributes["skills"] + ", " + attributes["interests"]
+    career_embeddings[career] = get_embedding(combined_text)
 
-    # Return a list of career recommendations
-    return recommendations if recommendations else ["No matching career found."]
+# Function to recommend careers based on skills and interests
+def get_career_recommendations(skills: str, interests: str):
+    user_input = skills + ", " + interests
+    user_embedding = get_embedding(user_input)
+    
+    recommendations = []
+    for career, career_embedding in career_embeddings.items():
+        similarity = torch.cosine_similarity(user_embedding, career_embedding, dim=0).item()
+        recommendations.append((career, similarity))
+    
+    recommendations.sort(key=lambda x: x[1], reverse=True)
+    
+    return [f"{career} (Similarity: {similarity:.2f})" for career, similarity in recommendations[:5]]