Update app.py

app.py

@@ -8,20 +8,32 @@ model_name = "Salesforce/codegen-350M-mono"  # Fastest model for code review
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name).to("cpu")  # Force CPU mode
 
+import tempfile
+
 def review_code(code_snippet):
     print("✅ Received Code:", code_snippet)  # Debugging log
     
-    # ✅ Add a clear instruction to the model
-    prompt = f"### Instruction: Review and fix the Python code below.\n### Input Code:\n{code_snippet}\n### Fixed Code:\n"
+    # ✅ Better instruction prompt
+    prompt = f"""
+### Instruction:
+You are a Python code reviewer. Your job is to analyze and fix errors in the provided Python code. 
+Make necessary corrections such as adding missing return statements, fixing syntax errors, and correcting logical mistakes. 
+Do NOT generate new functions or extra text—only return the fixed version of the provided code.
+
+### Input Code:
+{code_snippet}
+
+### Reviewed Code:
+"""
 
     # Process input
     inputs = tokenizer(prompt, return_tensors="pt").to("cpu")  # Move to CPU
     outputs = model.generate(
         **inputs,
-        max_length=80,  # ✅ Increased length for full function review
+        max_length=60,  # ✅ Keeps response concise & correct
         do_sample=False,
-        num_beams=5,  # ✅ Higher beams for better decision-making
-        repetition_penalty=1.8  # ✅ Lower penalty to avoid weird token removal
+        num_beams=4,  # ✅ Ensures better correction quality
+        repetition_penalty=2.5  # ✅ Prevents repeated/unnecessary output
     )
 
     # Check if the model generated output