Create app.py

app.py

@@ -0,0 +1,158 @@
+import gradio as gr
+from transformers import AutoProcessor, AutoModelForCausalLM
+import re
+from PIL import Image 
+import os
+import numpy as np
+
+import spaces
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+model = AutoModelForCausalLM.from_pretrained('thwri/CogFlorence-2.1-Large', trust_remote_code=True).to("cuda").eval()
+processor = AutoProcessor.from_pretrained('thwri/CogFlorence-2.1-Large', trust_remote_code=True)
+
+TITLE = "# [thwri/CogFlorence-2.1-Large]"
+DESCRIPTION = "microsoft/Florence-2-large tuned on Ejafa/ye-pop captioned with CogVLM2"
+
+def modify_caption(caption: str) -> str:
+    special_patterns = [
+        (r'the image is , ''),
+        (r'the image captures ', ''),
+        (r'the image showcases ', '')
+        (r'the image shows ', '')
+        (r'the image ', '')
+    ]
+    
+    for pattern, replacement in special_patterns:
+        caption = re.sub(pattern, replacement, caption, flags=re.IGNORECASE)
+    
+    caption = caption.replace('\n', '').replace('\r', '')
+    caption = re.sub(r'(?<=[.,?!])(?=[^\s])', r' ', caption)
+    caption = ' '.join(caption.strip().splitlines())
+    
+    return caption
+
+@spaces.GPU
+def process_image(image):
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    elif isinstance(image, str):
+        image = Image.open(image)
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+    
+    prompt = "<MORE_DETAILED_CAPTION>"
+    
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        num_beams=3,
+        do_sample=True
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = processor.post_process_generation(generated_text, task=prompt, image_size=(image.width, image.height))
+    return modify_caption(parsed_answer["<MORE_DETAILED_CAPTION>"])
+
+def extract_frames(image_path, output_folder):
+    with Image.open(image_path) as img:
+        base_name = os.path.splitext(os.path.basename(image_path))[0]
+        frame_paths = []
+        
+        try:
+            for i in range(0, img.n_frames):
+                img.seek(i)
+                frame_path = os.path.join(output_folder, f"{base_name}_frame_{i:03d}.png")
+                img.save(frame_path)
+                frame_paths.append(frame_path)
+        except EOFError:
+            pass  # We've reached the end of the sequence
+        
+        return frame_paths
+
+def process_folder(folder_path):
+    if not os.path.isdir(folder_path):
+        return "Invalid folder path."
+    
+    processed_files = []
+    skipped_files = []
+    for filename in os.listdir(folder_path):
+        if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp', '.webp', '.heic')):
+            image_path = os.path.join(folder_path, filename)
+            txt_filename = os.path.splitext(filename)[0] + '.txt'
+            txt_path = os.path.join(folder_path, txt_filename)
+            
+            # Check if the corresponding text file already exists
+            if os.path.exists(txt_path):
+                skipped_files.append(f"Skipped {filename} (text file already exists)")
+                continue
+            
+            # Check if the image has multiple frames
+            with Image.open(image_path) as img:
+                if getattr(img, "is_animated", False) and img.n_frames > 1:
+                    # Extract frames
+                    frames = extract_frames(image_path, folder_path)
+                    for frame_path in frames:
+                        frame_txt_filename = os.path.splitext(os.path.basename(frame_path))[0] + '.txt'
+                        frame_txt_path = os.path.join(folder_path, frame_txt_filename)
+                        
+                        # Check if the corresponding text file for the frame already exists
+                        if os.path.exists(frame_txt_path):
+                            skipped_files.append(f"Skipped {os.path.basename(frame_path)} (text file already exists)")
+                            continue
+                        
+                        caption = process_image(frame_path)
+                        
+                        with open(frame_txt_path, 'w', encoding='utf-8') as f:
+                            f.write(caption)
+                        
+                        processed_files.append(f"Processed {os.path.basename(frame_path)} -> {frame_txt_filename}")
+                else:
+                    # Process single image
+                    caption = process_image(image_path)
+                    
+                    with open(txt_path, 'w', encoding='utf-8') as f:
+                        f.write(caption)
+                    
+                    processed_files.append(f"Processed {filename} -> {txt_filename}")
+    
+    result = "\n".join(processed_files + skipped_files)
+    return result if result else "No image files found or all files were skipped in the specified folder."
+
+css = """
+#output { height: 500px; overflow: auto; border: 1px solid #ccc; }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(TITLE)
+    gr.Markdown(DESCRIPTION)
+    
+    with gr.Tab(label="Single Image Processing"):
+        with gr.Row():
+            with gr.Column():
+                input_img = gr.Image(label="Input Picture")
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
+        
+        gr.Examples(
+            [["image1.jpg"], ["image2.jpg"], ["image3.png"], ["image4.jpg"], ["image5.jpg"], ["image6.PNG"]],
+            inputs=[input_img],
+            outputs=[output_text],
+            fn=process_image,
+            label='Try captioning on below examples'
+        )
+        
+        submit_btn.click(process_image, [input_img], [output_text])
+
+    with gr.Tab(label="Batch Processing"):
+        with gr.Row():
+            folder_input = gr.Textbox(label="Input Folder Path")
+            batch_submit_btn = gr.Button(value="Process Folder")
+        batch_output = gr.Textbox(label="Batch Processing Results", lines=10)
+        
+        batch_submit_btn.click(process_folder, [folder_input], [batch_output])
+
+demo.launch(debug=True)