Update app.py

app.py

@@ -109,8 +109,24 @@ def inference_sentence(text):
     output = "This sentence will be processed:\n" + text
     return output
 
+def inference_dataset(path):
+    output = "This dataset will be processed:\n" + path
+    return output
+    
 
+"""
+inputs = [gr.File(
+            label="Upload a dataset"),
+          gr.CheckboxGroup(
+            ["emotion frequencies", "emotion distribution over time", "peaks", "topics"],
+            label = "Select options")]
 
+outputs = [gr.File(),
+           gr.Plot(label="Emotion frequencies"),
+           gr.Textbox(label="Emotion distribution over time"),
+           gr.Textbox(label="Peaks"),
+           gr.Textbox(label="Topics")]
+"""
 
 with gr.Blocks() as demo:
     with gr.Tab("Sentence"):
@@ -129,16 +145,18 @@ with gr.Blocks() as demo:
     with gr.Tab("Dataset"):
         gr.Markdown("""
         # Demo EmotioNL
-        This demo allows you to analyse the emotion in a sentence.
+        This demo allows you to analyse the emotions in a dataset. The data should be in tsv-format with two named columns: the first column (id) should contain the sentence IDs, and the second column (text) should contain the actual texts. Optionally, there is a third column named 'date', which specifies the date associated with the text (e.g., tweet date). This column is necessary when the options 'emotion distribution over time' and 'peaks' are selected.
         """)
         with gr.Row():
             with gr.Column():
-                input = gr.Textbox(
-                        label="Enter a sentence",
-                        lines=1)
-                send_btn = gr.Button("Send")
+                input_file = gr.File(
+                    label="Upload a dataset"),
+                    send_btn = gr.Button("Send")
+                input_checks = gr.CheckboxGroup(
+                    ["emotion frequencies", "emotion distribution over time", "peaks", "topics"],
+                    label = "Select options")
             output = gr.Textbox()
-        send_btn.click(fn=inference_sentence, inputs=input, outputs=output)
+        send_btn.click(fn=inference_sentence, inputs=input_file, outputs=output)
 
 demo.launch()