Update utils/suggestions.py

utils/suggestions.py

@@ -1,42 +1,34 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
+from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-# Load model & tokenizer once (cache on HF Spaces)
-tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
+# Load ungated Phi-2 model
+model_name = "microsoft/phi-2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(
-    "mistralai/Mistral-7B-Instruct-v0.1",
-    torch_dtype=torch.float32,
-    device_map="auto"
+    model_name,
+    torch_dtype=torch.float32
 )
+model.eval()
 
-def call_llm(prompt: str) -> str:
-    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
-
+def call_llm(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
     with torch.no_grad():
-        output_ids = model.generate(
-            input_ids,
-            max_new_tokens=256,
+        outputs = model.generate(
+            inputs["input_ids"],
+            max_new_tokens=250,
             do_sample=True,
-            top_p=0.95,
-            temperature=0.7
+            temperature=0.7,
+            top_k=50
         )
-    output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-
-    # Remove prompt from output if repeated
-    return output.replace(prompt, "").strip()
-
-def get_certification_suggestions(text: str) -> str:
-    prompt = f"Suggest top professional certifications to help advance this person's career:\n{text}"
-    return call_llm(prompt)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-def get_higher_education_suggestions(text: str) -> str:
-    prompt = f"Suggest suitable higher education programs or fields based on this CV:\n{text}"
-    return call_llm(prompt)
+def get_certification_suggestions(cv_text):
+    prompt = f"""You are a helpful career assistant. Analyze the following CV content and suggest relevant certifications to improve the candidate's chances of getting international jobs.
 
-def get_visa_recommendations(text: str) -> str:
-    prompt = f"Based on this CV, suggest possible countries and visa pathways to work or study abroad:\n{text}"
-    return call_llm(prompt)
+CV:
+{cv_text}
 
-def get_career_advice(text: str) -> str:
-    prompt = f"You are a career counselor. Give detailed personalized career advice based on the following CV:\n{text}"
-    return call_llm(prompt)
+List the top 5 certifications with short descriptions.
+"""
+    response = call_llm(prompt)
+    return response.strip()