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app.py

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+"""
+PlainEnglish-1B Gradio Demo App
+Interactive text generation interface for HuggingFace Spaces and ModelScope Studio.
+"""
+
+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_ID = "PlainEnglish-1B"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float32,
+    trust_remote_code=True,
+)
+model.eval()
+
+
+def generate_text(
+    prompt,
+    max_new_tokens=200,
+    temperature=0.7,
+    top_p=0.9,
+    top_k=50,
+    repetition_penalty=1.1,
+):
+    if not prompt.strip():
+        return "Please enter a prompt."
+
+    inputs = tokenizer(prompt, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=int(max_new_tokens),
+            temperature=float(temperature),
+            top_p=float(top_p),
+            top_k=int(top_k),
+            repetition_penalty=float(repetition_penalty),
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+
+    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated
+
+
+def count_parameters():
+    total = sum(p.numel() for p in model.parameters())
+    return f"{total:,} ({total/1e6:.1f}M)"
+
+
+css = """
+footer { display: none !important; }
+.gradio-container { max-width: 800px; margin: auto; }
+"""
+
+with gr.Blocks(css=css, title="PlainEnglish-1B") as demo:
+    gr.Markdown(
+        """
+        # PlainEnglish-1B
+        A 1B parameter text generation model fine-tuned for clear, plain English.
+        Enter a prompt and adjust parameters to generate text.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=3):
+            prompt_input = gr.Textbox(
+                label="Prompt",
+                placeholder="Enter your text prompt here...",
+                lines=4,
+            )
+        with gr.Column(scale=1):
+            max_tokens = gr.Slider(
+                minimum=10,
+                maximum=500,
+                value=200,
+                step=10,
+                label="Max New Tokens",
+            )
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=2.0,
+                value=0.7,
+                step=0.1,
+                label="Temperature",
+            )
+            top_p = gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.9,
+                step=0.05,
+                label="Top-p",
+            )
+            top_k = gr.Slider(
+                minimum=1,
+                maximum=100,
+                value=50,
+                step=5,
+                label="Top-k",
+            )
+            rep_penalty = gr.Slider(
+                minimum=1.0,
+                maximum=2.0,
+                value=1.1,
+                step=0.05,
+                label="Repetition Penalty",
+            )
+
+    generate_btn = gr.Button("Generate Text", variant="primary")
+
+    output_text = gr.Textbox(
+        label="Generated Text",
+        lines=8,
+    )
+
+    gr.Markdown(f"**Model Parameters**: {count_parameters()}")
+
+    generate_btn.click(
+        fn=generate_text,
+        inputs=[
+            prompt_input,
+            max_tokens,
+            temperature,
+            top_p,
+            top_k,
+            rep_penalty,
+        ],
+        outputs=output_text,
+    )
+
+    prompt_input.submit(
+        fn=generate_text,
+        inputs=[
+            prompt_input,
+            max_tokens,
+            temperature,
+            top_p,
+            top_k,
+            rep_penalty,
+        ],
+        outputs=output_text,
+    )
+
+    gr.Examples(
+        examples=[
+            ["The meaning of life is"],
+            ["In the year 2025, artificial intelligence"],
+            ["The best way to learn programming"],
+            ["Scientists recently discovered that"],
+            ["Once upon a time, in a small village"],
+        ],
+        inputs=prompt_input,
+    )
+
+if __name__ == "__main__":
+    demo.launch()