Update app.py

app.py

@@ -22,7 +22,6 @@ from transformers import (
     TextIteratorStreamer,
 )
 from transformers.image_utils import load_image
-from transformers.generation import GenerationConfig
 
 from docling_core.types.doc import DoclingDocument, DocTagsDocument
 
@@ -80,126 +79,148 @@ model_g = Qwen2_5_VLForConditionalGeneration.from_pretrained(
 ).to(device).eval()
 
 # Preprocessing functions for SmolDocling-256M
-
 def add_random_padding(image, min_percent=0.1, max_percent=0.10):
     """Add random padding to an image based on its size."""
     image = image.convert("RGB")
     width, height = image.size
-    pad_w = int(width * random.uniform(min_percent, max_percent))
-    pad_h = int(height * random.uniform(min_percent, max_percent))
-    corner_pixel = image.getpixel((0, 0))
-    padded = ImageOps.expand(image, border=(pad_w, pad_h, pad_w, pad_h), fill=corner_pixel)
-    return padded
-
+    pad_w_percent = random.uniform(min_percent, max_percent)
+    pad_h_percent = random.uniform(min_percent, max_percent)
+    pad_w = int(width * pad_w_percent)
+    pad_h = int(height * pad_h_percent)
+    corner_pixel = image.getpixel((0, 0))  # Top-left corner
+    padded_image = ImageOps.expand(image, border=(pad_w, pad_h, pad_w, pad_h), fill=corner_pixel)
+    return padded_image
 
 def normalize_values(text, target_max=500):
-    """Normalize numerical lists in text to a target maximum."""
-    def norm_list(vals):
-        m = max(vals) if vals else 1
-        return [round(v / m * target_max) for v in vals]
+    """Normalize numerical values in text to a target maximum."""
+    def normalize_list(values):
+        max_value = max(values) if values else 1
+        return [round((v / max_value) * target_max) for v in values]
 
-    def repl(m):
-        lst = ast.literal_eval(m.group(0))
-        return "".join(f"<loc_{n}>" for n in norm_list(lst))
-
-    return re.sub(r"\[([\d\.\s,]+)\]", repl, text)
+    def process_match(match):
+        num_list = ast.literal_eval(match.group(0))
+        normalized = normalize_list(num_list)
+        return "".join([f"<loc_{num}>" for num in normalized])
 
+    pattern = r"\[([\d\.\s,]+)\]"
+    normalized_text = re.sub(pattern, process_match, text)
+    return normalized_text
 
 def downsample_video(video_path):
-    """Extract 10 evenly spaced frames (with timestamps) from a video."""
-    cap = cv2.VideoCapture(video_path)
-    total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    frames, indices = [], np.linspace(0, total - 1, 10, dtype=int)
-    for idx in indices:
-        cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))
-        ok, img = cap.read()
-        if not ok:
-            continue
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        frames.append((Image.fromarray(img), round(idx / fps, 2)))
-    cap.release()
+    """Downsample a video to evenly spaced frames, returning PIL images with timestamps."""
+    vidcap = cv2.VideoCapture(video_path)
+    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = vidcap.get(cv2.CAP_PROP_FPS)
+    frames = []
+    frame_indices = np.linspace(0, total_frames - 1, 10, dtype=int)
+    for i in frame_indices:
+        vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
+        success, image = vidcap.read()
+        if success:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(image)
+            timestamp = round(i / fps, 2)
+            frames.append((pil_image, timestamp))
+    vidcap.release()
     return frames
 
-# Dolphin-specific inference
-
+# Dolphin-specific functions
 def model_chat(prompt, image):
-    proc = processor_k
-    mdl = model_k
-    device_str = "cuda" if torch.cuda.is_available() else "cpu"
-
-    # encode image
-    inputs = proc(image, return_tensors="pt").to(device_str).pixel_values.half()
-    # encode prompt
-    pi = proc.tokenizer(f"<s>{prompt} <Answer/>", add_special_tokens=False, return_tensors="pt").to(device_str)
-
-    # build generation config
-    gen_cfg = GenerationConfig.from_model_config(mdl.config)
-    gen_cfg.max_length = 4096
-    gen_cfg.min_length = 1
-    gen_cfg.use_cache = True
-    gen_cfg.bad_words_ids = [[proc.tokenizer.unk_token_id]]
-    gen_cfg.num_beams = 1
-    gen_cfg.do_sample = False
-    gen_cfg.repetition_penalty = 1.1
-
-    out = mdl.generate(
-        pixel_values=inputs,
-        decoder_input_ids=pi.input_ids,
-        decoder_attention_mask=pi.attention_mask,
-        generation_config=gen_cfg,
+    """Use Dolphin model for inference."""
+    processor = processor_k
+    model = model_k
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    inputs = processor(image, return_tensors="pt").to(device)
+    pixel_values = inputs.pixel_values.half()
+    prompt_inputs = processor.tokenizer(
+        f"<s>{prompt} <Answer/>",
+        add_special_tokens=False,
+        return_tensors="pt"
+    ).to(device)
+    outputs = model.generate(
+        pixel_values=pixel_values,
+        decoder_input_ids=prompt_inputs.input_ids,
+        decoder_attention_mask=prompt_inputs.attention_mask,
+        min_length=1,
+        max_length=4096,
+        pad_token_id=processor.tokenizer.pad_token_id,
+        eos_token_id=processor.tokenizer.eos_token_id,
+        use_cache=False,  # Changed to False to avoid deprecation warning
+        bad_words_ids=[[processor.tokenizer.unk_token_id]],
         return_dict_in_generate=True,
+        do_sample=False,
+        num_beams=1,
+        repetition_penalty=1.1
     )
-    seq = proc.tokenizer.batch_decode(out.sequences, skip_special_tokens=False)[0]
-    return seq.replace(f"<s>{prompt} <Answer/>", "").replace("<pad>", "").replace("</s>", "").strip()
-
-
-def process_elements(layout_result, image):
+    sequence = processor.tokenizer.batch_decode(outputs.sequences, skip_special_tokens=False)[0]
+    cleaned = sequence.replace(f"<s>{prompt} <Answer/>", "").replace("<pad>", "").replace("</s>", "").strip()
+    return cleaned
+
+def process_elements(layout_results, image):
+    """Parse layout results and extract elements from the image."""
+    # Placeholder parsing logic based on expected Dolphin output
+    # Assuming layout_results is a string like "[(x1,y1,x2,y2,label), ...]"
     try:
-        elements = ast.literal_eval(layout_result)
+        elements = ast.literal_eval(layout_results)
     except:
-        elements = []
-
-    results, order = [], 0
+        elements = []  # Fallback if parsing fails
+    
+    recognition_results = []
+    reading_order = 0
+    
     for bbox, label in elements:
-        x1, y1, x2, y2 = map(int, bbox)
-        crop = image.crop((x1, y1, x2, y2))
-        if crop.width == 0 or crop.height == 0:
+        try:
+            x1, y1, x2, y2 = map(int, bbox)
+            cropped = image.crop((x1, y1, x2, y2))
+            if cropped.size[0] > 0 and cropped.size[1] > 0:
+                if label == "text":
+                    text = model_chat("Read text in the image.", cropped)
+                    recognition_results.append({
+                        "label": label,
+                        "bbox": [x1, y1, x2, y2],
+                        "text": text.strip(),
+                        "reading_order": reading_order
+                    })
+                elif label == "table":
+                    table_text = model_chat("Parse the table in the image.", cropped)
+                    recognition_results.append({
+                        "label": label,
+                        "bbox": [x1, y1, x2, y2],
+                        "text": table_text.strip(),
+                        "reading_order": reading_order
+                    })
+                elif label == "figure":
+                    recognition_results.append({
+                        "label": label,
+                        "bbox": [x1, y1, x2, y2],
+                        "text": "[Figure]",  # Placeholder for figure content
+                        "reading_order": reading_order
+                    })
+            reading_order += 1
+        except Exception as e:
+            print(f"Error processing element: {e}")
             continue
-
-        if label == "text":
-            txt = model_chat("Read text in the image.", crop)
-        elif label == "table":
-            txt = model_chat("Parse the table in the image.", crop)
-        else:
-            txt = "[Figure]"
-
-        results.append({
-            "label": label,
-            "bbox": [x1, y1, x2, y2],
-            "text": txt.strip(),
-            "reading_order": order
-        })
-        order += 1
-
-    return results
-
-
-def generate_markdown(recog):
-    md = ""
-    for el in sorted(recog, key=lambda x: x["reading_order"]):
-        if el["label"] == "text":
-            md += el["text"] + "\n\n"
-        elif el["label"] == "table":
-            md += f"**Table:**\n{el['text']}\n\n"
-        else:
-            md += el["text"] + "\n\n"
-    return md.strip()
+    
+    return recognition_results
+
+def generate_markdown(recognition_results):
+    """Generate markdown from extracted elements."""
+    markdown = ""
+    for element in sorted(recognition_results, key=lambda x: x["reading_order"]):
+        if element["label"] == "text":
+            markdown += f"{element['text']}\n\n"
+        elif element["label"] == "table":
+            markdown += f"**Table:**\n{element['text']}\n\n"
+        elif element["label"] == "figure":
+            markdown += f"{element['text']}\n\n"
+    return markdown.strip()
 
 def process_image_with_dolphin(image):
-    layout = model_chat("Parse the reading order of this document.", image)
-    elems  = process_elements(layout, image)
-    return generate_markdown(elems)
+    """Process a single image with Dolphin model."""
+    layout_output = model_chat("Parse the reading order of this document.", image)
+    elements = process_elements(layout_output, image)
+    markdown_content = generate_markdown(elements)
+    return markdown_content
 
 @spaces.GPU
 def generate_image(model_name: str, text: str, image: Image.Image,
@@ -208,78 +229,83 @@ def generate_image(model_name: str, text: str, image: Image.Image,
                    top_p: float = 0.9,
                    top_k: int = 50,
                    repetition_penalty: float = 1.2):
+    """Generate responses for image input using the selected model."""
     if model_name == "ByteDance-s-Dolphin":
         if image is None:
             yield "Please upload an image."
-        else:
-            yield process_image_with_dolphin(image)
-        return
-
-    if model_name == "Nanonets-OCR-s":
-        proc, mdl = processor_m, model_m
-    elif model_name == "SmolDocling-256M-preview":
-        proc, mdl = processor_x, model_x
-    elif model_name == "MonkeyOCR-Recognition":
-        proc, mdl = processor_g, model_g
+            return
+        markdown_content = process_image_with_dolphin(image)
+        yield markdown_content
     else:
-        yield "Invalid model selected."
-        return
-
-    if image is None:
-        yield "Please upload an image."
-        return
-
-    imgs = [image]
-    if model_name == "SmolDocling-256M-preview":
-        if any(tok in text for tok in ["OTSL", "code"]):
-            imgs = [add_random_padding(img) for img in imgs]
-        if any(tok in text for tok in ["OCR at text", "Identify element", "formula"]):
-            text = normalize_values(text, target_max=500)
-
-    messages = [
-        {"role":"user",
-         "content":[{"type":"image"} for _ in imgs] + [{"type":"text","text":text}]
-        }
-    ]
-    prompt = proc.apply_chat_template(messages, add_generation_prompt=True)
-    inputs = proc(text=prompt, images=imgs, return_tensors="pt").to(device)
-
-    gen_cfg = GenerationConfig.from_model_config(mdl.config)
-    gen_cfg.max_new_tokens      = max_new_tokens
-    gen_cfg.temperature         = temperature
-    gen_cfg.top_p               = top_p
-    gen_cfg.top_k               = top_k
-    gen_cfg.repetition_penalty  = repetition_penalty
-    gen_cfg.use_cache           = True
-
-    streamer = TextIteratorStreamer(proc, skip_prompt=True, skip_special_tokens=True)
-    gen_kwargs = {
-        **inputs,
-        "streamer": streamer,
-        "generation_config": gen_cfg,
-    }
-
-    thread = Thread(target=mdl.generate, kwargs=gen_kwargs)
-    thread.start()
-
-    buffer = ""
-    full_output = ""
-    for new_text in streamer:
-        full_output += new_text
-        buffer     += new_text.replace("<|im_end|>", "")
-        yield buffer
-
-    if model_name == "SmolDocling-256M-preview":
-        cleaned = full_output.replace("<end_of_utterance>", "").strip()
-        if any(tag in cleaned for tag in ["<doctag>","<otsl>","<code>","<chart>","<formula>"]):
-            if "<chart>" in cleaned:
-                cleaned = cleaned.replace("<chart>","<otsl>").replace("</chart>","</otsl>")
-                cleaned = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', cleaned)
-            tags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned], imgs)
-            doc = DoclingDocument.load_from_doctags(tags_doc, document_name="Document")
-            yield f"**MD Output:**\n\n{doc.export_to_markdown()}"
+        # Existing logic for other models
+        if model_name == "Nanonets-OCR-s":
+            processor = processor_m
+            model = model_m
+        elif model_name == "MonkeyOCR-Recognition":
+            processor = processor_g
+            model = model_g
+        elif model_name == "SmolDocling-256M-preview":
+            processor = processor_x
+            model = model_x
         else:
-            yield cleaned
+            yield "Invalid model selected."
+            return
+
+        if image is None:
+            yield "Please upload an image."
+            return
+
+        images = [image]
+
+        if model_name == "SmolDocling-256M-preview":
+            if "OTSL" in text or "code" in text:
+                images = [add_random_padding(img) for img in images]
+            if "OCR at text at" in text or "Identify element" in text or "formula" in text:
+                text = normalize_values(text, target_max=500)
+
+        messages = [
+            {
+                "role": "user",
+                "content": [{"type": "image"} for _ in images] + [
+                    {"type": "text", "text": text}
+                ]
+            }
+        ]
+        prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
+
+        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+        generation_kwargs = {
+            **inputs,
+            "streamer": streamer,
+            "max_new_tokens": max_new_tokens,
+            "temperature": temperature,
+            "top_p": top_p,
+            "top_k": top_k,
+            "repetition_penalty": repetition_penalty,
+        }
+        thread = Thread(target=model.generate, kwargs=generation_kwargs)
+        thread.start()
+
+        buffer = ""
+        full_output = ""
+        for new_text in streamer:
+            full_output += new_text
+            buffer += new_text.replace("<|im_end|>", "")
+            yield buffer
+
+        if model_name == "SmolDocling-256M-preview":
+            cleaned_output = full_output.replace("<end_of_utterance>", "").strip()
+            if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
+                if "<chart>" in cleaned_output:
+                    cleaned_output = cleaned_output.replace("<chart>", "<otsl>").replace("</chart>", "</otsl>")
+                    cleaned_output = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', cleaned_output)
+                doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
+                doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
+                markdown_output = doc.export_to_markdown()
+                yield f"**MD Output:**\n\n{markdown_output}"
+            else:
+                yield cleaned_output
 
 @spaces.GPU
 def generate_video(model_name: str, text: str, video_path: str,
@@ -288,77 +314,97 @@ def generate_video(model_name: str, text: str, video_path: str,
                    top_p: float = 0.9,
                    top_k: int = 50,
                    repetition_penalty: float = 1.2):
+    """Generate responses for video input using the selected model."""
     if model_name == "ByteDance-s-Dolphin":
-        if not video_path:
+        if video_path is None:
             yield "Please upload a video."
             return
-        md_list = []
-        for frame, _ in downsample_video(video_path):
-            md_list.append(process_image_with_dolphin(frame))
-        yield "\n\n".join(md_list)
-        return
-
-    if model_name == "Nanonets-OCR-s":
-        proc, mdl = processor_m, model_m
-    elif model_name == "SmolDocling-256M-preview":
-        proc, mdl = processor_x, model_x
-    elif model_name == "MonkeyOCR-Recognition":
-        proc, mdl = processor_g, model_g
+        frames = downsample_video(video_path)
+        markdown_contents = []
+        for frame, _ in frames:
+            markdown_content = process_image_with_dolphin(frame)
+            markdown_contents.append(markdown_content)
+        combined_markdown = "\n\n".join(markdown_contents)
+        yield combined_markdown
     else:
-        yield "Invalid model selected."
-        return
-
-    if not video_path:
-        yield "Please upload a video."
-        return
-
-    frames = [f for f, _ in downsample_video(video_path)]
-    imgs = frames
-    if model_name == "SmolDocling-256M-preview":
-        if any(tok in text for tok in ["OTSL", "code"]):
-            imgs = [add_random_padding(img) for img in imgs]
-        if any(tok in text for tok in ["OCR at text", "Identify element", "formula"]):
-            text = normalize_values(text, target_max=500)
-
-    messages = [
-        {"role":"user",
-         "content":[{"type":"image"} for _ in imgs] + [{"type":"text","text":text}]
+        # Existing logic for other models
+        if model_name == "Nanonets-OCR-s":
+            processor = processor_m
+            model = model_m
+        elif model_name == "MonkeyOCR-Recognition":
+            processor = processor_g
+            model = model_g
+        elif model_name == "SmolDocling-256M-preview":
+            processor = processor_x
+            model = model_x
+        else:
+            yield "Invalid model selected."
+            return
+
+        if video_path is None:
+            yield "Please upload a video."
+            return
+
+        frames = downsample_video(video_path)
+        images = [frame for frame, _ in frames]
+
+        if model_name == "SmolDocling-256M-preview":
+            if "OTSL" in text or "code" in text:
+                images = [add_random_padding(img) for img in images]
+            if "OCR at text at" in text or "Identify element" in text or "formula" in text:
+                text = normalize_values(text, target_max=500)
+
+        messages = [
+            {
+                "role": "user",
+                "content": [{"type": "image"} for _ in images] + [
+                    {"type": "text", "text": text}
+                ]
+            }
+        ]
+        prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
+
+        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+        generation_kwargs = {
+            **inputs,
+            "streamer": streamer,
+            "max_new_tokens": max_new_tokens,
+            "temperature": temperature,
+            "top_p": top_p,
+            "top_k": top_k,
+            "repetition_penalty": repetition_penalty,
         }
-    ]
-    prompt = proc.apply_chat_template(messages, add_generation_prompt=True)
-    inputs = proc(text=prompt, images=imgs, return_tensors="pt").to(device)
-
-    gen_cfg = GenerationConfig.from_model_config(mdl.config)
-    gen_cfg.max_new_tokens      = max_new_tokens
-    gen_cfg.temperature         = temperature
-    gen_cfg.top_p               = top_p
-    gen_cfg.top_k               = top_k
-    gen_cfg.repetition_penalty  = repetition_penalty
-    gen_cfg.use_cache           = True
-
-    streamer = TextIteratorStreamer(proc, skip_prompt=True, skip_special_tokens=True)
-    gen_kwargs = {
-        **inputs,
-        "streamer": streamer,
-        "generation_config": gen_cfg,
-    }
-
-    thread = Thread(target=mdl.generate, kwargs=gen_kwargs)
-    thread.start()
-
-    buff = ""
-    full = ""
-    for nt in streamer:
-        full += nt
-        buff += nt.replace("<|im_end|>", "")
-        yield buff
-
-# Gradio UI
+        thread = Thread(target=model.generate, kwargs=generation_kwargs)
+        thread.start()
+
+        buffer = ""
+        full_output = ""
+        for new_text in streamer:
+            full_output += new_text
+            buffer += new_text.replace("<|im_end|>", "")
+            yield buffer
+
+        if model_name == "SmolDocling-256M-preview":
+            cleaned_output = full_output.replace("<end_of_utterance>", "").strip()
+            if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
+                if "<chart>" in cleaned_output:
+                    cleaned_output = cleaned_output.replace("<chart>", "<otsl>").replace("</chart>", "</otsl>")
+                    cleaned_output = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', cleaned_output)
+                doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
+                doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
+                markdown_output = doc.export_to_markdown()
+                yield f"**MD Output:**\n\n{markdown_output}"
+            else:
+                yield cleaned_output
+
+# Define examples for image and video inference
 image_examples = [
     ["Convert this page to docling", "images/1.png"],
     ["OCR the image", "images/2.jpg"],
     ["Convert this page to docling", "images/3.png"],
 ]
+
 video_examples = [
     ["Explain the ad in detail", "example/1.mp4"],
     ["Identify the main actions in the coca cola ad...", "example/2.mp4"]
@@ -374,6 +420,7 @@ css = """
 }
 """
 
+# Create the Gradio Interface
 with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
     gr.Markdown("# **[Core OCR](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
     with gr.Row():
@@ -408,6 +455,7 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
                 label="Select Model",
                 value="Nanonets-OCR-s"
             )
+            
     image_submit.click(
         fn=generate_image,
         inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],