Update model.py

model.py

@@ -1,4 +1,5 @@
 from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
 def modelFeedback(ats_score, resume_data, job_description):
     """
@@ -29,12 +30,26 @@ def modelFeedback(ats_score, resume_data, job_description):
         #### Job Description: {job_description}
         """
 
-    pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B")
-    # Generate the response using the pipeline
+
+    # Load the tokenizer and model
+    tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
+    model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
     try:
-        output = pipe(input_prompt, max_length=1500, temperature=0.01, top_p=0.7, return_full_text=True)
-        response_text = output[0]['generated_text']  # Extract generated text from the pipeline's output
+        # Tokenize the input
+        input_ids = tokenizer.encode(input_prompt, return_tensors="pt")
+        
+        # Generate the output
+        output = model.generate(
+            input_ids,
+            max_length=1500,
+            temperature=0.01,
+            top_p=0.7,
+            pad_token_id=tokenizer.eos_token_id  # Ensure padding works properly
+        )
+        
+        # Decode the output
+        response_text = tokenizer.decode(output[0], skip_special_tokens=True)
         return response_text
     except Exception as e:
-        print(f"Pipeline generation error: {e}")
+        print(f"Error during generation: {e}")
         return "Error: Unable to generate feedback."