Update src/florence/model.py

src/florence/model.py

@@ -1,87 +1,87 @@
-# Importing necessary libraries
-import sys
-import subprocess
-from typing import Optional
-from PIL import Image
-import gradio as gr
-import spaces
-from transformers import AutoProcessor, AutoModelForCausalLM
-
-# Local imports
-from src.logger import logging
-from src.exception import CustomExceptionHandling
-
-
-# Install the required dependencies
-subprocess.run(
-    "pip install flash-attn --no-build-isolation",
-    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
-    shell=True,
-)
-
-# Load model and processor from Hugging Face
-model_id = "microsoft/Florence-2-large-ft"
-try:
-    model = (
-        AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
-        .to("cuda")
-        .eval()
-    )
-    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
-    logging.info("Model and processor loaded successfully.")
-
-# Handle exceptions that may occur during the process
-except Exception as e:
-    # Custom exception handling
-    raise CustomExceptionHandling(e, sys) from e
-
-
-@spaces.GPU
-def run_example(
-    task_prompt: str, image: Image.Image, text_input: Optional[str] = None
-) -> str:
-    """
-    Runs an example using the given task prompt and image.
-
-    Args:
-        - task_prompt (str): The task prompt for the example.
-        - image (PIL.Image.Image): The image to be processed.
-        - text_input (str, optional): Additional text input to be appended to the task prompt. Defaults to None.
-
-    Returns:
-        str: The parsed answer generated by the model.
-    """
-    try:
-        # Check if image is None
-        if image is None:
-            gr.Warning("Please provide an image.")
-
-        # If there is no text input, use the task prompt as the prompt
-        prompt = task_prompt if text_input is None else task_prompt + text_input
-
-        # Process the image and text input
-        inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
-
-        # Generate the answer using the model
-        generated_ids = model.generate(
-            input_ids=inputs["input_ids"],
-            pixel_values=inputs["pixel_values"],
-            max_new_tokens=1024,
-            early_stopping=False,
-            do_sample=False,
-            num_beams=3,
-        )
-        generated_text = processor.batch_decode(
-            generated_ids, skip_special_tokens=False
-        )[0]
-        parsed_answer = processor.post_process_generation(
-            generated_text, task=task_prompt, image_size=(image.width, image.height)
-        )
-
-        # Return the parsed answer
-        return parsed_answer
-
-    # Handle exceptions that may occur during the process
-    except Exception as e:
-        # Custom exception handling
-        raise CustomExceptionHandling(e, sys) from e
+# Importing necessary libraries
+import sys
+import subprocess
+from typing import Optional
+from PIL import Image
+import gradio as gr
+import spaces
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+# Local imports
+from src.logger import logging
+from src.exception import CustomExceptionHandling
+
+
+# Install the required dependencies
+subprocess.run(
+    "pip install flash-attn --no-build-isolation",
+    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+    shell=True,
+)
+
+# Load model and processor from Hugging Face
+model_id = "microsoft/Florence-2-large-ft"
+try:
+    model = (
+        AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, from_tf=True)
+        .to("cuda")
+        .eval()
+    )
+    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+    logging.info("Model and processor loaded successfully.")
+
+# Handle exceptions that may occur during the process
+except Exception as e:
+    # Custom exception handling
+    raise CustomExceptionHandling(e, sys) from e
+
+
+@spaces.GPU
+def run_example(
+    task_prompt: str, image: Image.Image, text_input: Optional[str] = None
+) -> str:
+    """
+    Runs an example using the given task prompt and image.
+
+    Args:
+        - task_prompt (str): The task prompt for the example.
+        - image (PIL.Image.Image): The image to be processed.
+        - text_input (str, optional): Additional text input to be appended to the task prompt. Defaults to None.
+
+    Returns:
+        str: The parsed answer generated by the model.
+    """
+    try:
+        # Check if image is None
+        if image is None:
+            gr.Warning("Please provide an image.")
+
+        # If there is no text input, use the task prompt as the prompt
+        prompt = task_prompt if text_input is None else task_prompt + text_input
+
+        # Process the image and text input
+        inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
+
+        # Generate the answer using the model
+        generated_ids = model.generate(
+            input_ids=inputs["input_ids"],
+            pixel_values=inputs["pixel_values"],
+            max_new_tokens=1024,
+            early_stopping=False,
+            do_sample=False,
+            num_beams=3,
+        )
+        generated_text = processor.batch_decode(
+            generated_ids, skip_special_tokens=False
+        )[0]
+        parsed_answer = processor.post_process_generation(
+            generated_text, task=task_prompt, image_size=(image.width, image.height)
+        )
+
+        # Return the parsed answer
+        return parsed_answer
+
+    # Handle exceptions that may occur during the process
+    except Exception as e:
+        # Custom exception handling
+        raise CustomExceptionHandling(e, sys) from e