Update app.py

app.py

@@ -1,53 +1,12 @@
-import gradio as gr
+import streamlit as st
 from transformers import pipeline
-
-model = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-
-def sentiment_analysis(text):
-    res = model(text)[0]
-    res_label = {}
-    if res["label"] == "POSITIVE":
-        res_label["POSITIVE"] = res["score"]
-        res_label["NEGATIVE"] = 1 - res["score"]
-    if res["label"] == "NEGATIVE":
-        res_label["NEGATIVE"] = res["score"]
-        res_label["POSITIVE"] = 1 - res["score"]
-    return res_label
-
-custom_css = """
-#component-0 {
-    max-width: 600px;
-    margin: 0 auto;
-}
-
-h1,h2 {
-    text-align: center;
-}
-
-a {
-    color: #77b3ee !important;
-    text-decoration: none !important;
-}
-
-a:hover {
-    text-decoration: underline !important;
-}
-"""
-
-browser_tab_title = "Sentiment Analysis"
-intro_markdown = """## Sentiment Analysis
-
-Using the [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) model, trained on movie reviews."""
-
-with gr.Blocks(title=browser_tab_title, css=custom_css) as demo:
-    with gr.Row():
-        with gr.Column():
-            title = gr.Markdown(intro_markdown)
-            text_input = gr.Textbox(placeholder="Enter a positive or negative sentence here...", label="Text")
-            label_output = gr.Label(label="Sentiment outcome")
-            button_run = gr.Button("Compute sentiment")
-            button_run.click(sentiment_analysis, inputs=text_input, outputs=label_output)
-            gr.Examples(["That's great!", "The movie was bad.", "How are you"], text_input)
-
-if __name__ == "__main__":
-    demo.launch()
+from textblob import TextBlob
+
+pipe = pipeline('sentiment-analysis')
+st.title("Hugging Face Sentiment Analysis Spaces Example")
+st.subheader("What framework would you like to use for Sentiment Analysis")
+#Picking what NLP task you want to do
+option = st.selectbox('Framework',('Transformers', 'TextBlob')) #option is stored in this variable
+#Textbox for text user is entering
+st.subheader("Enter the text you'd like to analyze.")
+text = st.text_input('Enter text') #text is stored in this variable