Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import spaces
 import requests
 from PIL import Image
 import io
@@ -18,48 +17,49 @@ Compliment: You are the epitome of elegance and grace, with a style that is as t
 Conversation begins below:
 """
 
-# Function to generate compliment
 def generate_compliment(image):
     # Convert PIL image to bytes
     buffered = io.BytesIO()
     image.save(buffered, format="JPEG")
     image_bytes = buffered.getvalue()
     
+    # Connect to the captioning model on Hugging Face Spaces
+    captioning_url = "https://gokaygokay-sd3-long-captioner.hf.space/run/create_captions_rich"
     try:
-        # Connect to the captioning space
-        captioning_space = spaces.connect("gokaygokay/sd3-long-captioner")
-        
-        # Predict caption for the provided image
-        caption_response = captioning_space.predict("/create_captions_rich", { "image": image_bytes })
-        caption_text = caption_response.data[0]
-        
-    except Exception as e:
+        caption_response = requests.post(captioning_url, files={"image": ("image.jpg", image_bytes, "image/jpeg")})
+        caption_response.raise_for_status()  # Raise an exception for HTTP errors
+    except requests.exceptions.RequestException as e:
         return "Error", f"Failed to get caption. Exception: {e}"
     
     try:
-        # Connect to the LLM space
-        llm_space = spaces.connect("hysts/zephyr-7b")
-        
-        # Generate compliment using the caption
-        llm_payload = {
-            "system_prompt": SYSTEM_PROMPT,
-            "message": f"Caption: {caption_text}\nCompliment: ",
-            "max_new_tokens": 256,
-            "temperature": 0.7,
-            "top_p": 0.95,
-            "top_k": 50,
-            "repetition_penalty": 1,
-        }
-        
-        compliment_response = llm_space.run(llm_payload)
-        compliment_text = compliment_response.data[0]
-        
+        caption = caption_response.json()["data"][0]
     except Exception as e:
-        return "Error", f"Failed to generate compliment. Exception: {e}"
+        return "Error", f"Failed to parse caption response. Error: {str(e)}, Response: {caption_response.text}"
+    
+    # Connect to the LLM model on Hugging Face Spaces
+    llm_url = "https://hysts-zephyr-7b.hf.space/run/chat"
+    llm_payload = {
+        "system_prompt": SYSTEM_PROMPT,
+        "message": f"Caption: {caption}\nCompliment: ",
+        "max_new_tokens": 256,
+        "temperature": 0.7,
+        "top_p": 0.95,
+        "top_k": 50,
+        "repetition_penalty": 1,
+    }
+    try:
+        llm_response = requests.post(llm_url, json=llm_payload)
+        llm_response.raise_for_status()  # Raise an exception for HTTP errors
+    except requests.exceptions.RequestException as e:
+        return "Error", f"Failed to get compliment. Exception: {e}"
     
-    return caption_text, compliment_text
+    try:
+        compliment = llm_response.json()["data"][0]
+    except Exception as e:
+        return "Error", f"Failed to parse LLM response. Error: {str(e)}, Response: {llm_response.text}"
 
-# Gradio interface setup
+    return caption, compliment
+    
 # Gradio interface
 iface = gr.Interface(
     fn=generate_compliment,