Update app.py

app.py

@@ -1,25 +1,11 @@
 from gradio_client import Client, file
 import gradio as gr
 from PIL import Image
-import requests
 import io
 
 # Configuration for Hugging Face Spaces
 CAPTION_SPACE = "gokaygokay/SD3-Long-Captioner"
 LLM_SPACE = "hysts/zephyr-7b"
-SYSTEM_PROMPT = """
-You are a helpful assistant that gives the best compliments to people.
-You will be given a caption of someone's headshot.
-Based on that caption, provide a one sentence compliment to the person in the image.
-Make sure you compliment the person in the image and not any objects or scenery.
-Do NOT include any hashtags in your compliment or phrases like (emojis: dog, smiling face with heart-eyes, sun).
-Here are some examples of the desired behavior:
-Caption: a front view of a man who is smiling, there is a lighthouse in the background, there is a grassy area on the left that is green and curved. in the distance you can see the ocean and the shore. there is a grey and cloudy sky above the lighthouse and the trees.
-Compliment: Your smile is as bright as a lighthouse, lighting up the world around you. 🌟
-Caption: in a close-up, a blonde woman with short, wavy hair, is the focal point of the image. she's dressed in a dark brown turtleneck sweater, paired with a black hat and a black suit jacket. her lips are a vibrant red, and her eyes are a deep brown. in the background, a man with a black hat and a white shirt is visible.
-Compliment: You are the epitome of elegance and grace, with a style that is as timeless as your beauty. 💃🎩
-Conversation begins below:
-"""
 
 # Initialize Gradio client for captioning and language model
 captioning_client = Client(CAPTION_SPACE)
@@ -34,18 +20,16 @@ def generate_compliment(image):
     image.save(buffered, format="JPEG")
     image_bytes = buffered.getvalue()
     
-    # Connect to the captioning model on Hugging Face Spaces
+    # Retrieve caption from the captioning model
     try:
-        captioning_space = Client("gokaygokay/SD3-Long-Captioner")
-        caption_response = captioning_space.predict("/create_captions_rich", {"image": file(image_bytes)})
+        caption_response = captioning_client.predict("/create_captions_rich", {"image": file(image_bytes)})
         caption_text = caption_response.data[0]
     except Exception as e:
         return "Error", f"Failed to get caption. Exception: {str(e)}"
     
-    # Connect to the LLM model on Hugging Face Spaces
+    # Generate compliment using the language model
     try:
-        llm_space = Client("hysts/zephyr-7b")
-        llm_response = llm_space.predict({"system_prompt": "...", "message": f"Caption: {caption_text}\nCompliment: "})
+        llm_response = llm_client.predict({"system_prompt": SYSTEM_PROMPT, "message": f"Caption: {caption_text}\nCompliment: "})
         compliment_text = llm_response.data[0]
     except Exception as e:
         return "Error", f"Failed to generate compliment. Exception: {str(e)}"
@@ -61,5 +45,7 @@ iface = gr.Interface(
         gr.Textbox(label="Compliment")
     ],
     title="Compliment Bot 💖",
-    description="Upload your headshot and get a personalized compliment!"
+    description="Upload your headshot and get a personalized compliment!",
+    live=True  # Set live=True to launch the interface immediately
 )
+iface.launch()