Adapt app to older Python version

app.py

@@ -39,43 +39,42 @@ selected_dataset = st.sidebar.selectbox(
 
 
 def load_hf_dataset():
-    match selected_dataset:
-        case "clarin-pl/polemo2-official":
-            data = load_dataset("clarin-pl/polemo2-official")
-            DATA_DICT = {
-                "train": data["train"].to_pandas(),
-                "validation": data["validation"].to_pandas(),
-                "test": data["test"].to_pandas(),
-            }
-            DATA_DESCRIPTION = """The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, 
-            hotels, products, and university. It is human-annotated on a level of full reviews and individual 
-            sentences. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and 
-            sentence was manually annotated with sentiment in the 2+1 scheme, which gives a total of 197,
-            046 annotations. About 85% of the reviews are from the medicine and hotel domains. Each review is 
-            annotated with four labels: positive, negative, neutral, or ambiguous. """
-        case "laugustyniak/abusive-clauses-pl":
-            DATA_DICT = load_data()
-            DATA_DESCRIPTION = """
-            ''I have read and agree to the terms and conditions'' is one of the biggest lies on the Internet. 
-            Consumers rarely read the contracts they are required to accept. We conclude agreements over the Internet daily. 
-            But do we know the content of these agreements? Do we check potential unfair statements? On the Internet, 
-            we probably skip most of the Terms and Conditions. However, we must remember that we have concluded many more 
-            contracts. Imagine that we want to buy a house, a car, send our kids to the nursery, open a bank account, 
-            or many more. In all these situations, you will need to conclude the contract, but there is a high probability 
-            that you will not read the entire agreement with proper understanding. European consumer law aims to prevent 
-            businesses from using so-called ''unfair contractual terms'' in their unilaterally drafted contracts, 
-            requiring consumers to accept. 
+    if selected_dataset == "clarin-pl/polemo2-official":
+        data = load_dataset("clarin-pl/polemo2-official")
+        DATA_DICT = {
+            "train": data["train"].to_pandas(),
+            "validation": data["validation"].to_pandas(),
+            "test": data["test"].to_pandas(),
+        }
+        DATA_DESCRIPTION = """The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, 
+        hotels, products, and university. It is human-annotated on a level of full reviews and individual 
+        sentences. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and 
+        sentence was manually annotated with sentiment in the 2+1 scheme, which gives a total of 197,
+        046 annotations. About 85% of the reviews are from the medicine and hotel domains. Each review is 
+        annotated with four labels: positive, negative, neutral, or ambiguous. """
+    elif selected_dataset == "laugustyniak/abusive-clauses-pl":
+        DATA_DICT = load_data()
+        DATA_DESCRIPTION = """
+        ''I have read and agree to the terms and conditions'' is one of the biggest lies on the Internet. 
+        Consumers rarely read the contracts they are required to accept. We conclude agreements over the Internet daily. 
+        But do we know the content of these agreements? Do we check potential unfair statements? On the Internet, 
+        we probably skip most of the Terms and Conditions. However, we must remember that we have concluded many more 
+        contracts. Imagine that we want to buy a house, a car, send our kids to the nursery, open a bank account, 
+        or many more. In all these situations, you will need to conclude the contract, but there is a high probability 
+        that you will not read the entire agreement with proper understanding. European consumer law aims to prevent 
+        businesses from using so-called ''unfair contractual terms'' in their unilaterally drafted contracts, 
+        requiring consumers to accept. 
+    
+        Our dataset treats ''unfair contractual term'' as the equivalent of an abusive clause. It could be defined as a 
+        clause that is unilaterally imposed by one of the contract's parties, unequally affecting the other, or creating a 
+        situation of imbalance between the duties and rights of the parties. 
         
-            Our dataset treats ''unfair contractual term'' as the equivalent of an abusive clause. It could be defined as a 
-            clause that is unilaterally imposed by one of the contract's parties, unequally affecting the other, or creating a 
-            situation of imbalance between the duties and rights of the parties. 
-            
-            On the EU and at the national such as the Polish levels, agencies cannot check possible agreements by hand. Hence, 
-            we took the first step to evaluate the possibility of accelerating this process. We created a dataset and machine 
-            learning models to automate potentially abusive clauses detection partially. Consumer protection organizations and 
-            agencies can use these resources to make their work more effective and efficient. Moreover, consumers can automatically 
-            analyze contracts and understand what they agree upon. 
-            """
+        On the EU and at the national such as the Polish levels, agencies cannot check possible agreements by hand. Hence, 
+        we took the first step to evaluate the possibility of accelerating this process. We created a dataset and machine 
+        learning models to automate potentially abusive clauses detection partially. Consumer protection organizations and 
+        agencies can use these resources to make their work more effective and efficient. Moreover, consumers can automatically 
+        analyze contracts and understand what they agree upon. 
+        """
     return DATA_DICT, DATA_DESCRIPTION