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app.py

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+import gradio as gr
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Model details
+MODEL_NAME = "unsloth/DeepSeek-R1-Distill-Qwen-14B-bnb-4bit"
+SPACE_NAME = os.getenv("HF_SPACE_NAME", "qwen4bit")
+
+def generate_response(prompt, max_new_tokens=256):
+    """
+    This is a placeholder function that will be replaced with actual model inference
+    after fine-tuning is complete.
+    """
+    # Currently returns a placeholder message
+    return f"""[Placeholder Response]
+This is a demo of the {MODEL_NAME} model.
+Once fine-tuning is complete, this will respond to:
+"{prompt}"
+
+This space will be updated with the fine-tuned model."""
+
+# Create the Gradio interface
+with gr.Blocks(title=f"Fine-tuned {MODEL_NAME}") as demo:
+    gr.Markdown(f"""
+    # Fine-tuned DeepSeek-R1-Distill-Qwen-14B Model
+    
+    This space will host the fine-tuned version of `{MODEL_NAME}` once training is complete.
+    
+    **Model Details**:
+    - Base model: `{MODEL_NAME}`
+    - Fine-tuned on: `phi4-cognitive-dataset`
+    - 4-bit quantized (already, not further quantized)
+    
+    **Current Status**: Preparing for fine-tuning
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Enter your prompt",
+                placeholder="Type your prompt here...",
+                lines=4
+            )
+            max_tokens = gr.Slider(
+                minimum=32, 
+                maximum=1024, 
+                value=256, 
+                step=32,
+                label="Max new tokens"
+            )
+            submit_btn = gr.Button("Generate Response")
+        
+        with gr.Column():
+            output_text = gr.Textbox(
+                label="Model Response",
+                lines=10
+            )
+    
+    submit_btn.click(
+        fn=generate_response,
+        inputs=[input_text, max_tokens],
+        outputs=output_text
+    )
+    
+    gr.Markdown("""
+    ### Note
+    This is a placeholder application. The actual fine-tuned model will be deployed
+    to this space once training is complete.
+    """)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()