asd

main.py

@@ -0,0 +1,53 @@
+import gradio as gr
+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()