Update app.py

app.py

@@ -1,17 +1,29 @@
-import gradio as gr
+import streamlit as st
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-def respond_to_input(user_input):
-    return f"You said: {user_input}"
+# Load pre-trained model and tokenizer
+model_name = "gpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
 
-# Define the Gradio interface
-with gr.Blocks() as demo:
-    # Create a row layout with input and output textboxes
-    with gr.Row():
-        input_textbox = gr.Textbox(label="Input", placeholder="Type something...", lines=1)
-        output_textbox = gr.Textbox(label="Output", interactive=False, lines=1)
+def generate_text(prompt, max_length=50):
+    # Encode the input prompt
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    
+    # Generate text
+    outputs = model.generate(inputs, max_length=max_length, num_return_sequences=1)
+    
+    # Decode the generated text
+    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return text
 
-    # Link the input textbox to the output textbox
-    input_textbox.submit(respond_to_input, inputs=input_textbox, outputs=output_textbox)
+# Streamlit app
+st.title("GPT-2 Text Generator")
 
-# Launch the Gradio interface
-demo.launch()
+prompt = st.text_area("Input", "Once upon a time...")
+max_length = st.slider("Max Length", min_value=10, max_value=100, value=50)
+
+if st.button("Generate"):
+    generated_text = generate_text(prompt, max_length)
+    st.subheader("Generated Text")
+    st.write(generated_text)