Update app.py

app.py

@@ -4,9 +4,9 @@ import torch
 from PIL import Image
 from transformers import AutoProcessor, AutoModelForCausalLM
 
-import os
-import random
-from gradio_client import Client
+# import os
+# import random
+# from gradio_client import Client
 
 
 subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
@@ -16,7 +16,7 @@ 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")
+# api_key = os.getenv("HF_READ_TOKEN")
 
 def generate_caption(image):
     if not isinstance(image, Image.Image):
@@ -39,28 +39,30 @@ def generate_caption(image):
     )
     prompt =  parsed_answer["<MORE_DETAILED_CAPTION>"]
     print("Generation completed!:"+ prompt)
-    yield prompt, None
-    image_path = generate_image(prompt,random.randint(0, 4294967296))
-    yield prompt, image_path 
+    return prompt
+    # 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)}")
+# 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=3, show_copy_button = True),
-                             gr.Image(label="Output Image")]
+                  outputs = [gr.Textbox(label="Output Prompt", lines=2, show_copy_button = True),
+                             # gr.Image(label="Output Image")
+                            ]
                  )
 io.launch(debug=True)