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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+MODEL_ID = "tuf601121/scriptmodel"
+
+device = 0 if torch.cuda.is_available() else -1
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
+
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=device
+)
+
+def generate_story(prompt, max_tokens, temperature, top_p):
+    if not prompt.strip():
+        return "⚠️ Kuch likhiye pehle."
+
+    output = generator(
+        prompt,
+        max_new_tokens=max_tokens,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p,
+        repetition_penalty=1.2,
+        eos_token_id=tokenizer.eos_token_id
+    )
+
+    return output[0]["generated_text"]
+
+with gr.Blocks(title="ScriptModel – Hindi Story AI") as demo:
+    gr.Markdown("## ✍️ ScriptModel – Hindi Story Generator")
+    gr.Markdown("Ye model aapki Hindi kahaniyon ke style me likhta hai.")
+
+    prompt = gr.Textbox(
+        label="✏️ Prompt",
+        placeholder="एक गरीब किसान का बेटा शहर जाकर अपनी ज़िंदगी बदल देता है...",
+        lines=4
+    )
+
+    with gr.Row():
+        max_tokens = gr.Slider(50, 800, value=300, step=10, label="Length")
+        temperature = gr.Slider(0.2, 1.2, value=0.8, step=0.05, label="Creativity")
+        top_p = gr.Slider(0.5, 1.0, value=0.9, step=0.05, label="Top-p")
+
+    generate_btn = gr.Button("🚀 Generate Story")
+    output = gr.Textbox(label="📜 Output", lines=15)
+
+    generate_btn.click(
+        fn=generate_story,
+        inputs=[prompt, max_tokens, temperature, top_p],
+        outputs=output
+    )
+
+demo.launch()

requirements.txt

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+torch
+transformers
+gradio
+accelerate