v1

app.py

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+from transformers import GPT2Tokenizer, TFGPT2LMHeadModel, pipeline
+import gradio as gr
+
+
+model = TFGPT2LMHeadModel.from_pretrained("egosumkira/gpt2-fantasy")
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+story = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=0
+)
+
+
+def generate(tags_text, start_text="",  temp=1.0, n_beams=3, max_l=128):
+    tags = tags_text.split(", ")
+    prefix = f"~^{'^'.join(tags)}~@{start_text}"
+    g_text = story(prefix, temperature=1.0, max_length=128, repetition_penalty=7.0, num_beams=3)[0]['generated_text']
+    return g_text[g_text.find("@") + 1:]
+
+
+title = "GPT2 fantasy story generator"
+description = "This model can generate short fantasy story based on set of keywords and (optional) start of the text."
+
+title = "GPT2 fantasy story generator"
+description = "This model can generate short fantasy story based on set of keywords and (optional) start of the text."
+
+iface = gr.Interface(generate,
+	inputs = [
+    gr.Textbox(label="Keywords (comma separated)"),
+	gr.Textbox(label="Beggining of the text (optional)"),
+	gr.inputs.Slider(0, 2, default=1.0, step=0.05, label="Temperature"),
+	gr.Number(label="Max lenght", value=128),
+	gr.inputs.Slider(1, 10, default=3, label="Number of beams in Beam Search", step=1)
+	],
+	outputs = gr.Textbox(label="Output"),
+	title=title,
+    description=description
+    )
+
+
+if __name__ == "__main__":
+	iface.queue()
+    iface.launch()

requirements.txt

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+transformers==4.29.2