Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Initialize the text generation pipeline using the google/flan-t5-base model
+pipe = pipeline("text2text-generation", model="google/flan-t5-base")
+
+# Define a function to generate text using the Flan-T5 model
+def generate_text(input_text):
+    response = pipe(input_text)
+    return response[0]['generated_text']
+
+# Set up the Gradio interface
+iface = gr.Interface(
+    fn=generate_text,  # The function to generate text
+    inputs="text",  # Input type is a text field
+    outputs="text",  # Output is displayed as text
+    title="Flan-T5 Text Generation",  # Title of the interface
+    description="Enter text to generate a response using the google/flan-t5-base model."  # Description
+)
+
+# Launch the Gradio interface
+if __name__ == "__main__":
+    iface.launch(share=True)