sentimet.py

import gradio as gr
from transformers import pipeline

# Load pre-trained model from Hugging Face
classifier = pipeline('sentiment-analysis')

def classify_text(text):
result = classifier(text)[0]
label = result['label']
score = result['score']
return f"{label} (confidence: {score:.2f})"

# Create the Gradio interface
iface = gr.Interface(fn=classify_text, inputs=["text"], outputs=["prediction"])

# Launch the Gradio app
iface.launch()

sentiment.py

@@ -0,0 +1,17 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load pre-trained model from Hugging Face
+classifier = pipeline('sentiment-analysis')
+
+def classify_text(text):
+    result = classifier(text)[0]
+    label = result['label']
+    score = result['score']
+    return f"{label} (confidence: {score:.2f})"
+
+# Create the Gradio interface
+iface = gr.Interface(fn=classify_text, inputs=["text"], outputs=["prediction"])
+
+# Launch the Gradio app
+iface.launch()