Update app.py

app.py

@@ -59,16 +59,14 @@ with gr.Blocks() as demo:
         <h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin-top: 2rem; margin-bottom: 1.5rem">
         In the field of large language models, the challenge of fine-tuning has long perplexed researchers. Microsoft, however, has unveiled an innovative solution called <b>Low-Rank Adaptation (LoRA)</b>. With the emergence of behemoth models like GPT-3 boasting billions of parameters, the cost of fine-tuning them for specific tasks or domains has become exorbitant.
         LoRA offers a groundbreaking approach by freezing the weights of pre-trained models and introducing trainable layers known as <b>rank-decomposition matrices in each transformer block</b>. This ingenious technique significantly reduces the number of trainable parameters and minimizes GPU memory requirements, as gradients no longer need to be computed for the majority of model weights.        
+        <br>
+        <br>
+        You can find more info here: <a href="https://www.linkedin.com/pulse/fine-tuning-image-to-text-algorithms-with-lora-daniel-puente-viejo" target="_blank">Linkedin article</a>
         </h2>
-        <h3>
-        You can find more info here: [Linkedin article] (https://www.linkedin.com/pulse/fine-tuning-image-to-text-algorithms-withlora-daniel-puente-viejo)
-        </h3>
+        
         </div>
         """)
     gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)
-    gr.outputs.Textbox(type="text",label="Captions")
-    # gr.Image(label="Upload any Image", type = 'pil', optional=True)
-    # gr.Textbox(type="text",label="Captions")
-    
+    gr.outputs.Textbox(type="text",label="Captions")   
 
 demo.launch(debug=True)