try again

src/api_service/api/routers/recommend.py

@@ -354,5 +354,4 @@ def recommend_app(request: QueryRequest):
         print(e)
         traceback.print_exc()
         raise HTTPException(status_code=500, detail=str(e))
-
-
+

src/api_service/api/routers/summarize.py

@@ -25,8 +25,6 @@ async def process_pdf(
     Process a PDF file to extract and analyze privacy issues.
     """
     try:
-        logging.info("Received PDF for processing.")
-
         # Validate uploaded file
         if not pdf_file.filename.endswith(".pdf"):
             raise HTTPException(status_code=400, detail="A valid PDF file is required.")
@@ -36,8 +34,6 @@ async def process_pdf(
         with open(pdf_path, "wb") as pdf_out:
             pdf_out.write(await pdf_file.read())
 
-        logging.info("PDF saved to temporary storage. Starting processing.")
-
         # Process the PDF to extract privacy issues
         found_issues = process_pdf_privacy_issues(
             pdf_path, project_id, location_id, endpoint_id
@@ -46,8 +42,6 @@ async def process_pdf(
         # Store extracted issues in memory
         parsed_issues_storage["issues"] = found_issues
 
-        # logging.info(f"Processing completed. Found issues: {found_issues}")
-
         return {
             "message": "Processing completed successfully.",
             "found_issues": found_issues,
@@ -64,8 +58,6 @@ async def get_grade():
     Grade the parsed privacy issues.
     """
     try:
-        logging.info("Grading issues...")
-
         # Check if issues have been extracted
         if not parsed_issues_storage["issues"]:
             raise HTTPException(
@@ -85,8 +77,6 @@ async def get_grade():
         # Grade the issues
         report = grader.grade_privacy_issues(parsed_issues_storage["issues"])
 
-        # logging.info(f"Grading completed. Report: {report}")
-
         return {
             "overall_grade": report.overall_grade,
             "overall_score": report.overall_score,

src/models/tests/integration/test_api_endpoints.py

@@ -539,71 +539,71 @@ def mock_model():
     return mock
 
 def test_filter_by_genre(sample_df, mock_model):
-    with patch('your_module.df', sample_df), \
-         patch('your_module.model', mock_model):
+    with patch('recommend.df', sample_df), \
+         patch('recommend.model', mock_model):
         criteria = {'Genre': 'Social Media'}
         result = filter_dataframe(criteria)
         assert len(result) == 1
         assert result.iloc[0]['Service'] == 'App1'
 
 def test_filter_by_privacy_rating(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {'privacy_rating': 'B'}
         result = filter_dataframe(criteria)
         assert len(result) == 2
         assert set(result['Service']) == {'App1', 'App2'}
 
 def test_filter_by_content_rating(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {'Content Rating': 'Teen'}
         result = filter_dataframe(criteria)
         assert len(result) == 1
         assert result.iloc[0]['Service'] == 'App2'
 
 def test_filter_by_app_score(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {'app_score': 4.0}
         result = filter_dataframe(criteria)
         assert len(result) == 2
         assert set(result['Service']) == {'App1', 'App2'}
 
 def test_filter_by_installs(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {'Installs': 500000}
         result = filter_dataframe(criteria)
         assert len(result) == 2
         assert set(result['Service']) == {'App1', 'App2'}
 
 def test_filter_by_num_ratings(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {'num_ratings': 5000}
         result = filter_dataframe(criteria)
         assert len(result) == 2
         assert set(result['Service']) == {'App1', 'App2'}
 
 def test_filter_by_num_reviews(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {'num_reviews': 2500}
         result = filter_dataframe(criteria)
         assert len(result) == 2
         assert set(result['Service']) == {'App1', 'App2'}
 
 def test_filter_by_free(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {'Free': 'True'}
         result = filter_dataframe(criteria)
         assert len(result) == 2
         assert set(result['Service']) == {'App1', 'App3'}
 
 def test_filter_by_contains_ads(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {'Contains Ads': 'False'}
         result = filter_dataframe(criteria)
         assert len(result) == 1
         assert result.iloc[0]['Service'] == 'App2'
 
 def test_multiple_criteria(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {
             'privacy_rating': 'B',
             'Free': 'False',
@@ -614,13 +614,13 @@ def test_multiple_criteria(sample_df):
         assert result.iloc[0]['Service'] == 'App2'
 
 def test_no_matching_results(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {'privacy_rating': 'D'}
         result = filter_dataframe(criteria)
         assert len(result) == 0
 
 def test_service_criterion_updates_genre_and_privacy_rating(sample_df):
-    with patch('your_module.df', sample_df):
+    with patch('recommend.df', sample_df):
         criteria = {'Service': 'App2'}
         result = filter_dataframe(criteria)
         assert len(result) == 1