Update app.py

app.py

@@ -1,112 +1,89 @@
 import os
+import re
 import time
-import threading
+from threading import Thread
+
 import gradio as gr
 import spaces
 import torch
-import numpy as np
-from PIL import Image
-import cv2
+from PIL import Image, ImageDraw
+
 from transformers import (
     Qwen2_5_VLForConditionalGeneration,
-    Qwen2VLForConditionalGeneration,
-    Glm4vForConditionalGeneration,
     AutoProcessor,
     TextIteratorStreamer,
 )
-from qwen_vl_utils import process_vision_info
 
 # Constants for text generation
-MAX_MAX_NEW_TOKENS = 16384
-DEFAULT_MAX_NEW_TOKENS = 8192
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-# Load Camel-Doc-OCR-062825
-MODEL_ID_M = "prithivMLmods/Camel-Doc-OCR-062825"
-processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
-model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_M,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-# Load Megalodon-OCR-Sync-0713
-MODEL_ID_T = "prithivMLmods/Megalodon-OCR-Sync-0713"
-processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
-model_t = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_T,
+# Load Lumian2-VLR-7B-Thinking
+MODEL_ID_Y = "prithivMLmods/Lumian2-VLR-7B-Thinking"
+processor = AutoProcessor.from_pretrained(MODEL_ID_Y, trust_remote_code=True)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_Y,
     trust_remote_code=True,
     torch_dtype=torch.float16
 ).to(device).eval()
 
-# Load Video-MTR
-MODEL_ID_S = "Phoebe13/Video-MTR"
-processor_s = AutoProcessor.from_pretrained(MODEL_ID_S, trust_remote_code=True)
-model_s = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_S,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
 
-# Load ViLaSR
-MODEL_ID_Y = "inclusionAI/ViLaSR"
-processor_y = AutoProcessor.from_pretrained(MODEL_ID_Y, trust_remote_code=True)
-model_y = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_Y,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
+def extract_coordinates(text: str):
+    """Extract all (x, y) coordinates from model output text."""
+    pattern = r"\((\d+),\s*(\d+)\)"
+    coords = re.findall(pattern, text)
+    return [(int(x), int(y)) for x, y in coords]
+
+
+def draw_boxes(image: Image.Image, coords, box_type="solid"):
+    """Draw bounding boxes on the image."""
+    img_copy = image.copy()
+    draw = ImageDraw.Draw(img_copy)
+
+    side = 50  # square size
+    for (x, y) in coords:
+        box = [(x - side//2, y - side//2), (x + side//2, y + side//2)]
+        if box_type == "solid":
+            draw.rectangle(box, outline="red", width=3)
+        elif box_type == "dotted":
+            # Draw dotted (dashed) rectangle
+            dash_len = 5
+            x1, y1 = box[0]
+            x2, y2 = box[1]
+
+            # Top edge
+            for i in range(x1, x2, dash_len*2):
+                draw.line([(i, y1), (min(i+dash_len, x2), y1)], fill="blue", width=2)
+            # Bottom edge
+            for i in range(x1, x2, dash_len*2):
+                draw.line([(i, y2), (min(i+dash_len, x2), y2)], fill="blue", width=2)
+            # Left edge
+            for i in range(y1, y2, dash_len*2):
+                draw.line([(x1, i), (x1, min(i+dash_len, y2))], fill="blue", width=2)
+            # Right edge
+            for i in range(y1, y2, dash_len*2):
+                draw.line([(x2, i), (x2, min(i+dash_len, y2))], fill="blue", width=2)
+
+    return img_copy
 
-def downsample_video(video_path):
-    """
-    Downsample a video to evenly spaced frames, returning each as a PIL image with its timestamp.
-    """
-    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
 
-@spaces.GPU(duration=120)
-def generate_image(model_name: str, text: str, image: Image.Image,
-                  max_new_tokens: int = 1024,
-                  temperature: float = 0.6,
-                  top_p: float = 0.9,
-                  top_k: int = 50,
-                  repetition_penalty: float = 1.2):
+@spaces.GPU
+def generate_image(text: str, image: Image.Image,
+                   max_new_tokens: int = 1024,
+                   temperature: float = 0.6,
+                   top_p: float = 0.9,
+                   top_k: int = 50,
+                   repetition_penalty: float = 1.2,
+                   box_type: str = "solid"):
     """
-    Generate responses using the selected model for image input.
+    Generates responses using the Lumian2-VLR-7B-Thinking model for image input.
+    Yields raw text, Markdown-formatted text, and annotated image with bounding boxes.
     """
-    if model_name == "Camel-Doc-OCR-062825":
-        processor = processor_m
-        model = model_m
-    elif model_name == "Megalodon-OCR-Sync-0713":
-        processor = processor_t
-        model = model_t
-    elif model_name == "Video-MTR":
-        processor = processor_s
-        model = model_s
-    elif model_name == "ViLaSR-7B":
-        processor = processor_y
-        model = model_y
-    else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
-
     if image is None:
-        yield "Please upload an image.", "Please upload an image."
+        yield "Please upload an image.", "Please upload an image.", None
         return
 
     messages = [{
@@ -126,98 +103,38 @@ def generate_image(model_name: str, text: str, image: Image.Image,
         max_length=MAX_INPUT_TOKEN_LENGTH
     ).to(device)
     streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-    generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
-    thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        time.sleep(0.01)
-        yield buffer, buffer
-
-@spaces.GPU
-def generate_video(model_name: str, text: str, video_path: str,
-                  max_new_tokens: int = 1024,
-                  temperature: float = 0.6,
-                  top_p: float = 0.9,
-                  top_k: int = 50,
-                  repetition_penalty: float = 1.2):
-    """
-    Generate responses using the selected model for video input.
-    """
-    if model_name == "Camel-Doc-OCR-062825":
-        processor = processor_m
-        model = model_m
-    elif model_name == "Megalodon-OCR-Sync-0713":
-        processor = processor_t
-        model = model_t
-    elif model_name == "Video-MTR":
-        processor = processor_s
-        model = model_s
-    elif model_name == "ViLaSR-7B":
-        processor = processor_y
-        model = model_y
-    else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
-
-    if video_path is None:
-        yield "Please upload a video.", "Please upload a video."
-        return
-
-    frames = downsample_video(video_path)
-    messages = [
-        {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
-        {"role": "user", "content": [{"type": "text", "text": text}]}
-    ]
-    for frame in frames:
-        image, timestamp = frame
-        messages[1]["content"].append({"type": "text", "text": f"Frame {timestamp}:"})
-        messages[1]["content"].append({"type": "image", "image": image})
-    inputs = processor.apply_chat_template(
-        messages,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_dict=True,
-        return_tensors="pt",
-        truncation=False,
-        max_length=MAX_INPUT_TOKEN_LENGTH
-    ).to(device)
-    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
     generation_kwargs = {
         **inputs,
         "streamer": streamer,
         "max_new_tokens": max_new_tokens,
-        "do_sample": True,
         "temperature": temperature,
         "top_p": top_p,
         "top_k": top_k,
         "repetition_penalty": repetition_penalty,
+        "do_sample": True
     }
-    thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
     thread.start()
     buffer = ""
+    coords = []
     for new_text in streamer:
         buffer += new_text
-        buffer = buffer.replace("<|im_end|>", "")
+        coords = extract_coordinates(buffer)
+        annotated_image = draw_boxes(image, coords, box_type) if coords else None
         time.sleep(0.01)
-        yield buffer, buffer
+        yield buffer, buffer, annotated_image
 
-# Define examples for image and video inference
-image_examples = [
-    ["convert this page to doc [text] precisely for markdown.", "images/1.png"],
-    ["explain the movie shot in detail.", "images/5.jpg"],
-    ["convert this page to doc [table] precisely for markdown.", "images/2.png"],
-    ["explain the movie shot in detail.", "images/3.png"],
-    ["fill the correct numbers.", "images/4.png"]
-]
 
-video_examples = [
-    ["explain the video in detail.", "videos/b.mp4"],
-    ["explain the ad video in detail.", "videos/a.mp4"]
+# Define examples for image inference
+image_examples = [
+    ["Explain the content in detail.", "images/D.jpg"],
+    ["Explain the content (ocr).", "images/O.jpg"],
+    ["What is the core meaning of the poem?", "images/S.jpg"],
+    ["Provide a detailed caption for the image.", "images/A.jpg"],
+    ["Explain the pie-chart in detail.", "images/2.jpg"],
+    ["Jsonify Data.", "images/1.jpg"],
 ]
 
-# Updated CSS with model choice highlighting
 css = """
 .submit-btn {
     background-color: #2980b9 !important;
@@ -235,67 +152,54 @@ css = """
 
 # Create the Gradio Interface
 with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
-    gr.Markdown("# **[Multimodal VLM v1.0](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
+    gr.Markdown("# **Lumian2-VLR-7B-Thinking Image Inference**")
     with gr.Row():
         with gr.Column():
-            with gr.Tabs():
-                with gr.TabItem("Image Inference"):
-                    image_query = gr.Textbox(label="Query Input", placeholder="✦︎ Enter your query here...")
-                    image_upload = gr.Image(type="pil", label="Image")
-                    image_submit = gr.Button("Submit", elem_classes="submit-btn")
-                    gr.Examples(
-                        examples=image_examples,
-                        inputs=[image_query, image_upload]
-                    )
-                with gr.TabItem("Video Inference"):
-                    video_query = gr.Textbox(label="Query Input", placeholder="✦︎ Enter your query here...")
-                    video_upload = gr.Video(label="Video")
-                    video_submit = gr.Button("Submit", elem_classes="submit-btn")
-                    gr.Examples(
-                        examples=video_examples,
-                        inputs=[video_query, video_upload]
-                    )
+            gr.Markdown("## Image Inference")
+            image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
+            image_upload = gr.Image(type="pil", label="Image")
+            image_submit = gr.Button("Submit", elem_classes="submit-btn")
+            gr.Examples(
+                examples=image_examples,
+                inputs=[image_query, image_upload]
+            )
             with gr.Accordion("Advanced options", open=False):
                 max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
                 temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
                 top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
                 top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
                 repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2)
+
+            # New options for bounding box visualization
+            box_type = gr.Radio(
+                choices=["solid", "dotted"],
+                value="solid",
+                label="Bounding Box Style"
+            )
+
         with gr.Column():
             with gr.Column(elem_classes="canvas-output"):
                 gr.Markdown("## Output")
-                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=3)
+                output = gr.Textbox(label="Raw Output", interactive=False, lines=3, scale=2)
+
                 with gr.Accordion("(Result.md)", open=False):
-                    markdown_output = gr.Markdown(label="(Result.md)")
-            model_choice = gr.Radio(
-                choices=["Camel-Doc-OCR-062825", "Video-MTR", "Megalodon-OCR-Sync-0713", "ViLaSR-7B"],
-                label="Select Model",
-                value="Camel-Doc-OCR-062825"
+                    markdown_output = gr.Markdown()
+
+                annotated_output = gr.Image(label="Annotated Image with Bounding Boxes")
+
+            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Qwen2.5-VL/discussions)")
+
+            gr.Markdown(
+                """
+                > [Lumian2-VLR-7B-Thinking](https://huggingface.co/prithivMLmods/Lumian2-VLR-7B-Thinking): The Lumian2-VLR-7B-Thinking model is a high-fidelity vision-language reasoning (experimental model) system designed for fine-grained multimodal understanding. Built on Qwen2.5-VL-7B-Instruct, this model enhances image captioning, and document comprehension through explicit grounded reasoning. It produces structured reasoning traces aligned with visual coordinates, enabling explainable multimodal reasoning.
+                """
             )
-            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-v1.0/discussions)")
 
-            gr.Markdown("> [Camel-Doc-OCR-062825](https://huggingface.co/prithivMLmods/Camel-Doc-OCR-062825) is a Qwen2.5-VL-7B-Instruct finetune, highly optimized for document retrieval, structured extraction, analysis, and direct Markdown generation from images and PDFs.")
-            gr.Markdown("> [Megalodon-OCR-Sync-0713](https://huggingface.co/prithivMLmods/Megalodon-OCR-Sync-0713), finetuned from Qwen2.5-VL-3B-Instruct, specializes in context-aware multimodal document extraction and analysis, excelling at retrieval, layout parsing, math, and chart/table recognition.")
-            gr.Markdown("> [ViLaSR-7B](https://huggingface.co/inclusionAI/ViLaSR) focuses on reinforcing spatial reasoning in visual-language tasks by combining interwoven thinking with visual drawing, making it especially suited for spatial reasoning and complex tip-based queries.")
-            gr.Markdown("> [Video-MTR](https://huggingface.co/Phoebe13/Video-MTR) introduces reinforced multi-turn reasoning for long-form video understanding, enabling iterative key segment selection and deeper question comprehension.")
-       
-            gr.Markdown("> ✋ ViLaSR-7B - demo only supports text-only reasoning, which doesn't reflect the full behavior of the model and may underrepresent its capabilities.")            
-            gr.Markdown("> ⚠️ Note: Models in this space may not perform well on video inference tasks.")
-    # Define the submit button actions
-    image_submit.click(fn=generate_image,
-                       inputs=[
-                           model_choice, image_query, image_upload,
-                           max_new_tokens, temperature, top_p, top_k,
-                           repetition_penalty
-                       ],
-                       outputs=[output, markdown_output])
-    video_submit.click(fn=generate_video,
-                       inputs=[
-                           model_choice, video_query, video_upload,
-                           max_new_tokens, temperature, top_p, top_k,
-                           repetition_penalty
-                       ],
-                       outputs=[output, markdown_output])
+    image_submit.click(
+        fn=generate_image,
+        inputs=[image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty, box_type],
+        outputs=[output, markdown_output, annotated_output]
+    )
 
 if __name__ == "__main__":
-    demo.queue(max_size=40).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)
+    demo.queue(max_size=50).launch(share=True)