Update app.py

app.py

@@ -4,6 +4,9 @@ import torch
 from PIL import Image
 from transformers import AutoProcessor, AutoModelForCausalLM
 
+import os
+from gradio_client import Client
+
 
 subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
 
@@ -12,6 +15,8 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
 florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
 
+api_key = os.getenv("HF_READ_TOKEN")
+
 def generate_caption(image):
     if not isinstance(image, Image.Image):
         image = Image.fromarray(image)
@@ -31,10 +36,29 @@ def generate_caption(image):
         task="<MORE_DETAILED_CAPTION>",
         image_size=(image.width, image.height)
     )
-    return parsed_answer["<MORE_DETAILED_CAPTION>"]
+    prompt =  parsed_answer["<MORE_DETAILED_CAPTION>"]
+    yield prompt, None
+    image_path = generate_image(prompt,random.randint(0, 4294967296))
+    yield prompt, image_path 
+
+def generate_image(prompt, seed=42, width=1024, height=1024):
+    try:
+        result = Client("KingNish/Realtime-FLUX", hf_token=api_key).predict(
+            prompt=prompt,
+            seed=seed,
+            width=width,
+            height=height,
+            api_name="/generate_image"
+        )
+        # Extract the image path from the result tuple
+        image_path = result[0]
+        return image_path
+    except Exception as e:
+        raise Exception(f"Error generating image: {str(e)}")
 
 io = gr.Interface(generate_caption,
                   inputs=[gr.Image(label="Input Image")],
-                  outputs = [gr.Textbox(label="Output Prompt", lines=2, show_copy_button = True)]
+                  outputs = [gr.Textbox(label="Output Prompt", lines=2, show_copy_button = True),
+                             gr.Image(label="Output Image")]
                  )
 io.launch(debug=True)