Update app.py

app.py

@@ -4,25 +4,19 @@ import torch
 from PIL import Image
 from transformers import AutoProcessor, AutoModelForCausalLM
 
-# 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)
 
-# Initialize Florence model
+# Инициализация модели Florence
 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):
+def generate_ad_post(image):
     if not isinstance(image, Image.Image):
         image = Image.fromarray(image)
     
-    inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
+    # Измененный текст запроса для генерации рекламного поста
+    inputs = florence_processor(text="Создайте рекламный пост на русском языке", images=image, return_tensors="pt").to(device)
     generated_ids = florence_model.generate(
         input_ids=inputs["input_ids"],
         pixel_values=inputs["pixel_values"],
@@ -34,35 +28,15 @@ def generate_caption(image):
     generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
     parsed_answer = florence_processor.post_process_generation(
         generated_text,
-        task="<MORE_DETAILED_CAPTION>",
+        task="Создание рекламного поста",
         image_size=(image.width, image.height)
     )
-    prompt =  parsed_answer["<MORE_DETAILED_CAPTION>"]
-    print("\n\nGeneration completed!:"+ prompt)
+    prompt =  parsed_answer["Создание рекламного поста"]
+    print("\n\nГенерация завершена!:"+ prompt)
     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)}")
- 
-io = gr.Interface(generate_caption,
-                  inputs=[gr.Image(label="Input Image")],
-                  outputs = [gr.Textbox(label="Output Prompt", lines=2, show_copy_button = True),
-                             # gr.Image(label="Output Image")
-                            ]
+io = gr.Interface(generate_ad_post,
+                  inputs=[gr.Image(label="Входное изображение")],
+                  outputs=[gr.Textbox(label="Рекламный пост", lines=2, show_copy_button=True)]
                  )
 io.launch(debug=True)