Update app.py

app.py

@@ -1,23 +1,55 @@
 import gradio as gr
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+from transformers import pipeline
 
 def greet(name):
     return "Hello " + name + "!!"
 
-def flip_text(x):
-    return x[::-1]
+def classify(text):
+    return {"cat": 0.3, "dog": 0.7}
+
+def predict_sentiment(text, model):
+    if model == "finiteautomata/bertweet-base-sentiment-analysis":
+        pipe = pipeline("text-classification", model="finiteautomata/bertweet-base-sentiment-analysis")
+        out = pipe(text, return_all_scores=True)
+        return {pred["label"]: pred["score"] for pred in out}
+    elif model == "vader":
+        sia = SentimentIntensityAnalyzer()
+        return sia.polarity_scores(text)
+        
+
 
 demo = gr.Blocks()
 
 with demo:
-    gr.Markdown("Flip text or image files using this demo.")
+    gr.Markdown("A bunch of different Gradio demos in tabs.\n\nNote that generally, the code that is in each tab could be its own Gradio application!")
     with gr.Tabs():
-        with gr.TabItem("Flip Text"):
-            with gr.Row():
-                text_input = gr.Textbox()
-                text_output = gr.Textbox()
-            text_button = gr.Button("Flip")
-        with gr.TabItem("Flip Image"):
+        gr.Markdown('The most basic "Hello World"-type demo you can write')
+        with gr.TabItem("Basic Hello"):
             interface = gr.Interface(fn=greet, inputs="text", outputs="text")
+        with gr.TabItem("Label Output"):
+            gr.Markdown("An example of a basic interface with a classification label as output")
+            interface = gr.Interface(fn=classify, inputs="text", outputs="label")
+
+        with gr.TabItem("Multiple Inputs"):
+            gr.Markdown("A more complex interface for sentiment analysis with multiple inputs, including a dropdown, and some examples")
+            demo = gr.Interface(
+                sentence_builder,
+                [
+                    gr.TextBox(placeholder="Your text input"),
+                    gr.Dropdown(
+                        ["finiteautomata/bertweet-base-sentiment-analysis", "vader"], label="Model"
+                    ),
+                ],
+                "text",
+                examples=[
+                    ["Happy smile", "vader"],
+                    ["Happy smile", "finiteautomata/bertweet-base-sentiment-analysis"],
+                    ["Sad frown", "vader"],
+                    ["Sad frown", "finiteautomata/bertweet-base-sentiment-analysis"],
+                ]
+            )
+
 
     text_button.click(flip_text, inputs=text_input, outputs=text_output)