Update app.py

app.py

@@ -6,8 +6,8 @@ model_name = "google/flan-t5-large"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-def concatenate_and_generate(text1, text2, reranking, temperature, top_p):
-    concatenated_text = text1 + " " + text2
+def concatenate_and_generate(source_text, target_example_texts, reranking, temperature, top_p):
+    concatenated_text = source_text + " " + target_example_texts
     inputs = tokenizer(concatenated_text, return_tensors="pt")
     
     # Generate the output with specified temperature and top_p
@@ -22,20 +22,51 @@ def concatenate_and_generate(text1, text2, reranking, temperature, top_p):
     generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
     return generated_text
 
+# Preset examples with cached generations
+preset_examples = [
+    {
+        "source_text": "Once upon a time in a small village",
+        "target_example_texts": "In a land far away, there was a kingdom ruled by a wise king. Every day, the people of the kingdom would gather to listen to the king's stories, which were full of wisdom and kindness.",
+        "reranking": 5,
+        "temperature": 1.0,
+        "top_p": 1.0,
+        "output": "Once upon a time in a small village in a land far away, there was a kingdom ruled by a wise king. Every day, the people of the kingdom would gather to listen to the king's stories, which were full of wisdom and kindness."
+    },
+    {
+        "source_text": "The quick brown fox",
+        "target_example_texts": "A nimble, chocolate-colored fox swiftly darted through the emerald forest, weaving between trees with grace and agility.",
+        "reranking": 5,
+        "temperature": 0.9,
+        "top_p": 0.9,
+        "output": "The quick brown fox, a nimble, chocolate-colored fox, swiftly darted through the emerald forest, weaving between trees with grace and agility."
+    }
+]
+
 # Define Gradio interface
 with gr.Blocks(theme="ParityError/Interstellar@0.0.1") as demo:
     gr.Markdown("# TinyStyler Demo")
     gr.Markdown("Style transfer the source text into the target style, given some example texts of the target style. You can adjust re-ranking and top_p to your desire to control the quality of style transfer. A higher re-ranking value will generally result in better results, at slower speed.")
 
-    text1 = gr.Textbox(lines=3, placeholder="Enter the source text to transform into the target style...", label="Source Text")
-    text2 = gr.Textbox(lines=5, placeholder="Enter example texts of the target style (one per line)...", label="Example Texts of the Target Style")
+    source_text = gr.Textbox(lines=3, placeholder="Enter the source text to transform into the target style...", label="Source Text")
+    target_example_texts = gr.Textbox(lines=5, placeholder="Enter example texts of the target style (one per line)...", label="Example Texts of the Target Style")
     reranking = gr.Slider(1, 10, value=5, step=1, label="Re-ranking")
     temperature = gr.Slider(0.0, 2.0, value=1.0, step=0.1, label="Temperature")
     top_p = gr.Slider(0.0, 1.0, value=1.0, step=0.1, label="Top-P")
     
     output = gr.Markdown(label="Output")
+
+    def set_example(example):
+        return example["source_text"], example["target_example_texts"], example["reranking"], example["temperature"], example["top_p"], example["output"]
+
+    example_dropdown = gr.Dropdown(label="Preset Examples", choices=[f"Example {i+1}" for i in range(len(preset_examples))])
+    example_button = gr.Button("Load Example")
+    example_button.click(
+        lambda example_index: set_example(preset_examples[int(example_index.split()[-1])-1]),
+        inputs=[example_dropdown],
+        outputs=[source_text, target_example_texts, reranking, temperature, top_p, output]
+    )
     
     btn = gr.Button("Generate")
-    btn.click(concatenate_and_generate, [text1, text2, reranking, temperature, top_p], output)
+    btn.click(concatenate_and_generate, [source_text, target_example_texts, reranking, temperature, top_p], output)
 
 demo.launch()