the updated ones

app.py

@@ -6,7 +6,6 @@ from sklearn.preprocessing import LabelEncoder
 from sklearn.ensemble import RandomForestClassifier
 import joblib
 
-#
 # Load and preprocess data
 def load_and_preprocess_data(filename):
     df = pd.read_csv(filename)
@@ -66,7 +65,7 @@ def predict_colleges(category, gender, rank, region):
     filtered_df["College Name"] = label_encoders["College Name"].inverse_transform(filtered_df["College Name"].values)
     filtered_df["Branch"] = label_encoders["Branch"].inverse_transform(filtered_df["Branch"].values)
     
-    result = filtered_df[["College Name", "Branch"]].drop_duplicates().to_string(index=False)
+    result = filtered_df[["College Name", "Branch"]].drop_duplicates()
     return result
 
 # Gradio Interface
@@ -78,7 +77,7 @@ demo = gr.Interface(
         gr.Number(label="Rank"),
         gr.Dropdown(choices=["AU", "SV"], label="Region")
     ],
-    outputs="text",
+    outputs=gr.Dataframe(headers=["College Name", "Branch"]),
     title="AP EAMCET College Predictor",
     description="Enter your details to predict all possible colleges and branches based on your rank."
 )