Update app.py

app.py

@@ -21,46 +21,40 @@ load_dotenv()
 # ---------------------------------------------------------------------------
 # MODULE 3 — Pre-processing Module
 # ---------------------------------------------------------------------------
-def build_prompt(description: str, code: str) -> str:
-    prompt = f"""<start_of_turn>user
-### ROLE
-You are an expert Python code reviewer. Your sole task is to determine whether the
-provided Python code correctly implements the behaviour described in the user's
-requirements description.
-### CONTEXT
-Developers sometimes write code that does not fully satisfy the requirements they
-were given. You will analyse the semantic relationship between a natural-language
-description and a Python code snippet, then produce a structured evaluation report.
-### INPUT DATA
-**Requirements Description:**
-{description.strip()}
-**Python Code:**
-```python
-{code.strip()}
+def pre_processing(description, code):
+    prompt = f"""
+#ROLE
+You are a Python code reviewer. Your goal is to determine if the provided Python code strictly complies with the requirements mentioned by the user.
+ 
+#CONTEXT
+Developers may create code that does not entirely meet the specified requirements. You will examine the relationship between a natural-language description and a Python code sample and create a structured evaluation report.
+ 
+#INPUT DATA
+Description: {description}
+Code: ```python
+{code}
 ```
-### TASK
-1. Read the requirements description carefully.
-2. Analyse the Python code line by line.
+ 
+#TASK
+1. Carefully read the requirements description.
+2. Examine each line of the Python code.
 3. Determine whether the code fulfils ALL requirements stated in the description.
-4. Estimate an accuracy percentage (0–100) reflecting how completely the code
-   matches the description.
-5. List any specific requirements that are missing or incorrectly implemented.
-### CONSTRAINTS
-- Do NOT execute the code.
-- Base your evaluation solely on static code analysis and logical reasoning.
-- Keep feedback concise, clear, and actionable.
-- Your response MUST follow the output format exactly.
-### OUTPUT FORMAT
-Respond only with the following structure — no extra text before or after:
-RESULT: <PASS or FAIL>
-ACCURACY: <integer 0-100>%
-SUMMARY: <one sentence overall assessment>
-ISSUES:
-- <issue 1, or "None" if code fully matches the description>
-- <issue 2>
+4. Estimate the accuracy in percentages to assess how accurately the code matches the description.
+5. List specific requirements that are either absent or incorrectly applied.
+6. Base your evaluation solely on static code analysis and logical reasoning.
+ 
+#CONSTRAINTS
+1. Do not execute the code.
+2. The output must be in the specified format.
+3. The output should be clear and concise.
+ 
+#OUTPUT FORMAT (to be strictly followed without any changes)
+Result: either "Pass" or "Fail" (Pass if the code is functional and matches the description, Fail if it has bugs or doesn't match).
+Accuracy: an integer from 0 to 100 representing how well the code matches the description and is bug-free.
+Review: a short prose explanation of the result (e.g. "Code correctly handles all described requirements including edge cases.").    
+Issues: The value should be a single string containing a numbered list. For each item, include the severity (Error, Warning, or Info) followed by a dash and description of the issue (e.g. "Missing type hints on function signature"). List each issue on a new line starting with a number. Use newline characters (\n) to separate each line.
 ...
-<end_of_turn>
-<start_of_turn>model
+ 
 """
     return prompt