Create QUANTARION13-APP.PY

QUANTARION13-APP.PY

@@ -0,0 +1,58 @@
+import gradio as gr
+from transformers import pipeline
+
+# Model ID on Hugging Face
+MODEL_ID = "Aqarion13/QUANTARION-EDU-AI"
+
+# Create a text-generation pipeline
+generator = pipeline(
+    "text-generation",
+    model=MODEL_ID,
+    tokenizer=MODEL_ID,
+    # You can adjust max_length if you want longer outputs
+    max_length=150
+)
+
+def generate_text(prompt: str, max_length: int = 150):
+    """
+    Generate text from a prompt using the model.
+    """
+    if not prompt:
+        return "Please enter a prompt."
+    
+    # Generate output with sampling parameters
+    outputs = generator(
+        prompt,
+        max_length=max_length,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95
+    )
+    
+    # Return the generated text
+    return outputs[0]["generated_text"]
+
+# Build Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 QUANTARION Text Generation")
+    gr.Markdown(
+        "Enter a prompt below to generate text in English, Hindi, Chinese, or Spanish."
+    )
+
+    prompt_input = gr.Textbox(label="Input Prompt", lines=3)
+    max_length_input = gr.Slider(
+        minimum=50, maximum=500, step=10,
+        label="Max Output Length", value=150
+    )
+    generate_button = gr.Button("Generate")
+    output_text = gr.Textbox(label="Generated Text", lines=10)
+
+    # Bind UI interaction
+    generate_button.click(
+        fn=generate_text,
+        inputs=[prompt_input, max_length_input],
+        outputs=output_text,
+    )
+
+if __name__ == "__main__":
+    demo.launch()