Update app.py

app.py

@@ -2,63 +2,45 @@ import gradio as gr
 from share_btn import community_icon_html, loading_icon_html, share_js
 import re
 import os 
+
 hf_token = os.environ.get('HF_TOKEN')
 from gradio_client import Client
 client = Client("https://fffiloni-test-llama-api-debug.hf.space/", hf_token=hf_token)
-
 clipi_client = Client("https://fffiloni-clip-interrogator-2.hf.space/")
 
 def get_text_after_colon(input_text):
-    # Find the first occurrence of ":"
     colon_index = input_text.find(":")
-    
-    # Check if ":" exists in the input_text
     if colon_index != -1:
-        # Extract the text after the colon
         result_text = input_text[colon_index + 1:].strip()
         return result_text
     else:
-        # Return the original text if ":" is not found
         return input_text
 
-def infer(image_input, audience):
+def infer(image_input, audience, clip_caption, llama_prompt):
     gr.Info('Calling CLIP Interrogator ...')
     clipi_result = clipi_client.predict(
-    				image_input,	# str (filepath or URL to image) in 'parameter_3' Image component
-    				"best",	# str in 'Select mode' Radio component
-    				4,	# int | float (numeric value between 2 and 24) in 'best mode max flavors' Slider component
+    				image_input,
+    				"best",
+    				4,
     				api_name="/clipi2"
     )
-    print(clipi_result)
-   
+    clip_caption.value = clipi_result[0]
 
-    llama_q = f"""
-    I'll give you a simple image caption, please provide a fictional story for a {audience} audience that would fit well with the image. Please be creative, do not worry and only generate a cool fictional story. 
-    Here's the image description: 
-    '{clipi_result[0]}'
-    
-    """
+    llama_q = f"{llama_prompt} " + \
+              f"Here's the image description: '{clip_caption.value}'"
+              
     gr.Info('Calling Llama2 ...')
     result = client.predict(
-    				llama_q,	# str in 'Message' Textbox component
+    				llama_q,
                     "I2S",
     				api_name="/predict"
     )
-
-    print(f"Llama2 result: {result}")
-
     result = get_text_after_colon(result)
-
-    # Split the text into paragraphs based on actual line breaks
     paragraphs = result.split('\n')
-    
-    # Join the paragraphs back with an extra empty line between each paragraph
     formatted_text = '\n\n'.join(paragraphs)
-
-
     return formatted_text, gr.Group.update(visible=True)
 
-css="""
+css = """
 #col-container {max-width: 910px; margin-left: auto; margin-right: auto;}
 a {text-decoration-line: underline; font-weight: 600;}
 a {text-decoration-line: underline; font-weight: 600;}
@@ -129,11 +111,11 @@ with gr.Blocks(css=css) as demo:
             with gr.Column():
                 image_in = gr.Image(label="Image input", type="filepath", elem_id="image-in", height=420)
                 audience = gr.Radio(label="Target Audience", choices=["Children", "Adult"], value="Children")
+                clip_caption = gr.Textbox(label="CLIP Generated Caption", default="")
+                llama_prompt = gr.Textbox(label="Llama2 Prompt", default="I'll give you a simple image caption, please provide a fictional story for a {audience} audience that would fit well with the image. Please be creative, do not worry and only generate a cool fictional story.")
                 submit_btn = gr.Button('Tell me a story')
             with gr.Column():
-                #caption = gr.Textbox(label="Generated Caption")
-                story = gr.Textbox(label="generated Story", elem_id="story")
-
+                story = gr.Textbox(label="Generated Story", elem_id="story")
                 with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
                     community_icon = gr.HTML(community_icon_html)
                     loading_icon = gr.HTML(loading_icon_html)
@@ -141,12 +123,9 @@ with gr.Blocks(css=css) as demo:
         
         gr.Examples(examples=[["./examples/crabby.png", "Children"],["./examples/hopper.jpeg", "Adult"]],
                     fn=infer,
-                    inputs=[image_in, audience],
+                    inputs=[image_in, audience, clip_caption, llama_prompt],
                     outputs=[story, share_group],
                     cache_examples=True
                    )
         
-    submit_btn.click(fn=infer, inputs=[image_in, audience], outputs=[story, share_group])
-    share_button.click(None, [], [], _js=share_js)
-
-demo.queue(max_size=12).launch()
+    submit_btn.click(fn=infer, inputs=[image_in, audience, clip_caption, llama_prompt], outputs