Create app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Initialize the model and tokenizer
+model_name = "distilgpt2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Sample starting scenario and choices
+scenario = "You wake up in a strange forest. Paths lead in all directions."
+choices = [
+    "Walk north into the dense forest.",
+    "Go south back towards a distant town.",
+    "Explore east towards the sound of water.",
+    "Move west where the land rises."
+]
+
+def generate_continuation(choice):
+    prompt = f"{scenario} You decide to {choice}"
+    max_length = 50  # Adjust as needed
+
+    input_ids = tokenizer.encode(prompt, return_tensors='pt')
+    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
+    continuation = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    return continuation
+
+def update_scenario(choice_index):
+    chosen_action = choices[choice_index]
+    new_scenario = generate_continuation(chosen_action)
+    return new_scenario
+
+# Gradio app
+with gr.Blocks() as app:
+    with gr.Row():
+        story_display = gr.Textbox(value=scenario, show_label=False, readonly=True, lines=7)
+        choices_buttons = [gr.Button(choice, elem_id=f"choice_{i}") for i, choice in enumerate(choices)]
+    
+    def handle_choice(choice_index):
+        new_story = update_scenario(choice_index)
+        story_display.update(value=new_story)
+        return gr.update()
+
+    for i, button in enumerate(choices_buttons):
+        button.click(fn=handle_choice, inputs=i, outputs=story_display)
+
+app.launch()