Update app.py

app.py

@@ -2,12 +2,15 @@ import gradio as gr
 from transformers import pipeline
 
 playground = gr.Blocks()
-image_pipe = pipeline("image-to-text",
-                model="Salesforce/blip-image-captioning-base")
+image_pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+
+def launch_image_pipe(input):
+    out = image_pipe(input)
+    return out[0]['generated_text']
 
 def create_playground_header():
     gr.Markdown("""
-                #🤗 Hugging Face Labs
+                # 🤗 Hugging Face Labs
                 **Explore different LLM on Hugging Face platform. Just play and enjoy**
                 """)
 
@@ -16,10 +19,13 @@ def create_playground_footer():
                 **To Learn More about 🤗 Hugging Face, [Click Here](https://huggingface.co/docs)**
                 """)
 
-def create_tabs_header(description):
+def create_tabs_header(topic, description):
     with gr.Row():
         with gr.Column(scale=4):
-            gr.Markdown(f"**{description}**")
+            gr.Markdown(f"""
+                        ## {topic}
+                        > {description}
+                        """)
         with gr.Column(scale=1):
             test_pipeline_button = gr.Button(value="Process")
         return test_pipeline_button
@@ -28,7 +34,10 @@ with playground:
     create_playground_header()
     with gr.Tabs():
         with gr.TabItem("Image"):
-            image_pipeline_button = create_tabs_header("Image Captioning")
+            topic = "Image Captioning"
+            description = ["model='Salesforce/blip-image-captioning-base'"]
+            # image_pipeline_button = create_tabs_header("Image Captioning")
+            image_pipeline_button = create_tabs_header(topic, description)
             gr.Markdown("""
                         > model='Salesforce/blip-image-captioning-base'
                         """)
@@ -38,7 +47,7 @@ with playground:
                 with gr.Column():
                     generated_textbox = gr.Textbox(lines=2, placeholder="", label="Generated Text")
                     
-            image_pipeline_button.click(image_pipe,
+            image_pipeline_button.click(launch_image_pipe,
                                         inputs=[img],
                                         outputs=[generated_textbox])