Update app.py

app.py

@@ -1,47 +1,68 @@
 import gradio as gr
+import subprocess
 import torch
 from PIL import Image
-from transformers import AutoProcessor, AutoModel
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+# import os
+# import random
+# from gradio_client import Client
 
-# تحديد الجهاز
-device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# تحميل الموديل والمُعالِج (Processor)
-florence_model = AutoModel.from_pretrained(
-    "microsoft/Florence-2-base",
-    trust_remote_code=True
-).to(device).eval()
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+# Initialize Florence model
+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)
 
-florence_processor = AutoProcessor.from_pretrained(
-    "microsoft/Florence-2-base",
-    trust_remote_code=True
-)
+# api_key = os.getenv("HF_READ_TOKEN")
 
-# دالة توليد الوصف
 def generate_caption(image):
     if not isinstance(image, Image.Image):
         image = Image.fromarray(image)
     
-    # تجهيز البيانات للموديل
-    inputs = florence_processor(images=image, return_tensors="pt").to(device)
-    
-    # توليد النص
+    inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
     generated_ids = florence_model.generate(
+        input_ids=inputs["input_ids"],
         pixel_values=inputs["pixel_values"],
         max_new_tokens=1024,
-        num_beams=3
+        early_stopping=False,
+        do_sample=False,
+        num_beams=3,
     )
-    
-    # تحويل الـ IDs إلى نص
-    generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-    print("\nGeneration completed!:", generated_text)
-    return generated_text
-
-# واجهة Gradio
-io = gr.Interface(
-    fn=generate_caption,
-    inputs=gr.Image(label="Input Image"),
-    outputs=gr.Textbox(label="Output Prompt", lines=2, show_copy_button=True)
-)
+    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>",
+        image_size=(image.width, image.height)
+    )
+    prompt =  parsed_answer["<MORE_DETAILED_CAPTION>"]
+    print("\nGeneration completed!:"+ 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.launch(debug=True)