Update app.py

app.py

@@ -35,10 +35,10 @@ def generate_roast(resume_text):
     max_resume_tokens = 2048 - prompt_tokens
     
     # Tokenize and truncate the resume text to fit within the remaining token limit
-    resume_tokenized = tokenizer(resume_text, return_tensors="pt", truncation=True, max_length=max_resume_tokens)
+    resume_tokenized = tokenizer(resume_text, truncation=True, max_length=max_resume_tokens)
     
-    # Decode the truncated resume text back into a string
-    truncated_resume_text = tokenizer.decode(resume_tokenized['input_ids'][0], skip_special_tokens=True)
+    # Decode the truncated resume back into a string
+    truncated_resume_text = tokenizer.decode(resume_tokenized['input_ids'], skip_special_tokens=True)
     
     # Combine the prompt and the truncated resume text
     final_prompt = f"{prompt_text}{truncated_resume_text}\n\nRoast:"
@@ -46,6 +46,7 @@ def generate_roast(resume_text):
     # Generate roast with the truncated prompt
     generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
     
+    # Generate roast within max_new_tokens limit
     roast = generator(final_prompt, max_new_tokens=50, num_return_sequences=1)
     
     return roast[0]['generated_text']
@@ -54,6 +55,7 @@ def generate_roast(resume_text):
 
 
 
+
 # Gradio interface function
 def roast_resume(file):
     if file.name.endswith('.pdf'):