Update app.py

app.py

@@ -3,7 +3,6 @@ import random
 import uuid
 import json
 import time
-import re
 from threading import Thread
 
 import gradio as gr
@@ -12,7 +11,8 @@ import torch
 import numpy as np
 from PIL import Image
 import cv2
-import supervision as sv
+import requests
+import supervision as sv # Added for object detection visualization
 
 from transformers import (
     Qwen3VLMoeForConditionalGeneration,
@@ -50,7 +50,7 @@ processor_q3vl = AutoProcessor.from_pretrained(MODEL_ID_Q3VL, trust_remote_code=
 model_q3vl = Qwen3VLMoeForConditionalGeneration.from_pretrained(
     MODEL_ID_Q3VL,
     trust_remote_code=True,
-    torch_dtype=torch.float16  # Corrected from 'dtype' to 'torch_dtype'
+    torch_dtype=torch.float16
 ).to(device).eval()
 
 
@@ -129,7 +129,7 @@ def generate_video(text: str, video_path: str,
     messages = [{"role": "user", "content": [{"type": "text", "text": text}]}]
     images_for_processor = []
     for frame, timestamp in frames_with_ts:
-        messages[0]["content"].insert(0, {"type": "image"})
+        messages[0]["content"].insert(0, {"type": "image"}) 
         images_for_processor.append(frame)
 
     prompt_full = processor_q3vl.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
@@ -153,126 +153,107 @@ def generate_video(text: str, video_path: str,
         time.sleep(0.01)
         yield buffer, buffer
 
-# --- Object Detection & Pointing Functions ---
-
-def parse_model_output_for_coords(text_output, task_type):
-    """Parses text to find normalized coordinates using regex and json."""
-    match = re.search(r'\[\[.*?\]\]', text_output)
-    if not match:
-        return []
+# --- Object Detection Functions ---
 
+def create_annotated_image(image: Image.Image, json_data_string: str):
+    """Parses JSON from model and draws bounding boxes on the image."""
     try:
-        coords_str = match.group(0)
-        coords = json.loads(coords_str)
+        # Clean up the string to get pure JSON from markdown code blocks
+        if "```json" in json_data_string:
+            json_str = json_data_string.split("```json")[1].split("```").strip()
+        else:
+            json_str = json_data_string
         
-        if not isinstance(coords, list): return []
-        if task_type == "Object Detection":
-            return [c for c in coords if isinstance(c, list) and len(c) == 4 and all(isinstance(n, (int, float)) for n in c)]
-        elif task_type == "Point Detection":
-            return [c for c in coords if isinstance(c, list) and len(c) == 2 and all(isinstance(n, (int, float)) for n in c)]
-        return []
-    except (json.JSONDecodeError, TypeError):
-        return []
-
-def create_annotated_image_from_normalized(image, bboxes_normalized, object_name="Object"):
-    """Draws bounding boxes on an image from normalized coordinates."""
-    if not bboxes_normalized: return image
-    
-    original_width, original_height = image.size
-    annotated_image = np.array(image.convert("RGB"))
-    
-    bboxes_absolute = []
-    for bbox in bboxes_normalized:
-        x_min, y_min, x_max, y_max = bbox
-        bboxes_absolute.append([
-            int(x_min * original_width), int(y_min * original_height),
-            int(x_max * original_width), int(y_max * original_height)
-        ])
-    
-    detections = sv.Detections(xyxy=np.array(bboxes_absolute, dtype=np.float32))
-    bounding_box_annotator = sv.BoxAnnotator(thickness=2)
-    label_annotator = sv.LabelAnnotator(text_thickness=1, text_scale=0.5)
-    labels = [f"{object_name} #{i+1}" for i in range(len(detections))]
-    
-    annotated_image = bounding_box_annotator.annotate(scene=annotated_image, detections=detections)
-    annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)
-    
-    return Image.fromarray(annotated_image)
+        bbox_data = json.loads(json_str)
+        if not isinstance(bbox_data, list):
+            bbox_data = [bbox_data]
+            
+    except (json.JSONDecodeError, IndexError):
+        # If parsing fails, return the original image and an error message
+        return image, f"Failed to parse JSON from model output:\n{json_data_string}"
 
-def create_point_annotated_image_from_normalized(image, points_normalized):
-    """Draws points on an image from normalized coordinates."""
-    if not points_normalized: return image
-    
-    original_width, original_height = image.size
     annotated_image = np.array(image.convert("RGB"))
-    
-    points_absolute = [[int(p[0] * original_width), int(p[1] * original_height)] for p in points_normalized]
-    
-    points_array = np.array(points_absolute).reshape(1, -1, 2)
-    key_points = sv.KeyPoints(xy=points_array)
-    vertex_annotator = sv.VertexAnnotator(radius=5, color=sv.Color.RED)
-    annotated_image = vertex_annotator.annotate(scene=annotated_image, key_points=key_points)
-    
-    return Image.fromarray(annotated_image)
+    boxes = []
+    labels = []
 
-@spaces.GPU
-def generate_detection_and_pointing(image: Image.Image, prompt: str, task_type: str):
-    """Main function for the detection/pointing tab."""
-    if image is None: raise gr.Error("Please upload an image.")
-    if not prompt or not prompt.strip(): raise gr.Error("Please enter a prompt describing the object.")
+    for item in bbox_data:
+        if "box_2d" in item and "label" in item:
+            boxes.append(item["box_2d"])
+            labels.append(str(item["label"]))
+
+    if not boxes:
+        return image, "No bounding boxes with labels found in the model's output."
+
+    # Create supervision Detections object from the parsed data
+    detections = sv.Detections(xyxy=np.array(boxes))
+
+    # Create annotators
+    bounding_box_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
+    label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
+
+    # Annotate the image
+    annotated_image = bounding_box_annotator.annotate(
+        scene=annotated_image, detections=detections
+    )
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections, labels=labels
+    )
 
-    if task_type == "Object Detection":
-        instruction = f"You are a helpful detection assistant. Find all instances of '{prompt}' in the image. Provide their bounding box coordinates in the format [[x_min, y_min, x_max, y_max]]. The coordinates must be normalized between 0 and 1. Only output the list of coordinates."
-    else: # Point Detection
-        instruction = f"You are a helpful detection assistant. Find the center point of all instances of '{prompt}' in the image. Provide their coordinates in the format [[x, y]]. The coordinates must be normalized between 0 and 1. Only output the list of coordinates."
+    return Image.fromarray(annotated_image), json.dumps(bbox_data, indent=2)
 
-    messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": instruction}]}]
+@spaces.GPU
+def generate_detection(image: Image.Image, prompt: str):
+    """
+    Generates object detections using the Qwen3-VL model.
+    """
+    if image is None:
+        return None, "Please upload an image first."
+
+    # A detailed prompt to guide the model for object detection
+    detection_prompt = f"""
+This is an object detection task. Analyze the image to identify all instances of '{prompt}'.
+Respond ONLY with a JSON array where each object is a dictionary with two keys:
+1. "label": The name of the object found (e.g., "{prompt}").
+2. "box_2d": The bounding box coordinates as a list of four numbers [x_min, y_min, x_max, y_max].
+Do not include any other text or explanations outside of the final JSON code block.
+"""
+
+    messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": detection_prompt}]}]
     prompt_full = processor_q3vl.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    inputs = processor_q3vl(text=[prompt_full], images=[image], return_tensors="pt", padding=True).to(device)
-    
-    output_ids = model_q3vl.generate(**inputs, max_new_tokens=128, do_sample=False)
-    response_text = processor_q3vl.batch_decode(output_ids, skip_special_tokens=True)[0]
-    
-    parts = response_text.split("ASSISTANT:")
-    response_text = parts[-1].strip() if len(parts) > 1 else response_text.split("<|im_end|>")[-1].strip()
 
-    coords = parse_model_output_for_coords(response_text, task_type)
-    
-    annotated_image = image
-    if coords:
-        try:
-            if task_type == "Object Detection":
-                annotated_image = create_annotated_image_from_normalized(image, coords, prompt)
-            else:
-                annotated_image = create_point_annotated_image_from_normalized(image, coords)
-        except Exception as e:
-            response_text += f"\n\n[Error] Failed to draw annotations. Details: {e}"
-            annotated_image = image
-    else:
-        response_text += "\n\n[Info] Could not find or parse coordinates from model output. No annotations were drawn."
-
-    return annotated_image, response_text
+    inputs = processor_q3vl(
+        text=[prompt_full], images=[image], return_tensors="pt", padding=True
+    ).to(device)
 
-# --- Gradio UI ---
+    # Generate a static response (no streaming) for easier JSON parsing
+    generated_ids = model_q3vl.generate(**inputs, max_new_tokens=2048)
+    generated_ids_trimmed = generated_ids[:, inputs.input_ids.shape:]
+    response_text = processor_q3vl.batch_decode(generated_ids_trimmed, skip_special_tokens=True)
 
-#image_examples = [
-#    ["Describe the safety measures in the image. Conclude (Safe / Unsafe)..", "https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-Thinking/resolve/main/images/5.jpg"],
-#    ["Convert this page to doc [markdown] precisely.", "https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-Thinking/resolve/main/images/3.png"],
-#    ["Explain the creativity in the image.", "https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-Thinking/resolve/main/images/6.jpg"],
-#]
+    # Create annotated image from the model's response
+    annotated_image, formatted_json = create_annotated_image(image, response_text)
 
-#video_examples = [
-#    ["Explain the video in detail.", "https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-Thinking/resolve/main/videos/2.mp4"],
-#    ["Explain the ad in detail.", "https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-Thinking/resolve/main/videos/1.mp4"]
-#]
+    return annotated_image, formatted_json
 
-#detection_examples = [
-#    ["https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-Thinking/resolve/main/images/6.jpg", "Object Detection", "the person"],
-#    ["https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-Thinking/resolve/main/images/5.jpg", "Point Detection", "the fire extinguisher"],
+# --- Gradio UI ---
 
+# Define examples for image and video inference
+image_examples = [
+    ["Describe the safety measures in the image. Conclude (Safe / Unsafe)..", "examples/5.jpg"],
+    ["Convert this page to doc [markdown] precisely.", "examples/3.png"],
+    ["Explain the creativity in the image.", "examples/6.jpg"],
+]
 
-#]
+video_examples = [
+    ["Explain the video in detail.", "examples/2.mp4"],
+    ["Explain the ad in detail.", "examples/1.mp4"]
+]
 
+detection_examples = [
+    ["examples/detection_1.jpg", "person"],
+    ["examples/detection_2.jpg", "car"],
+    ["examples/detection_3.jpg", "cat"],
+]
 
 css = """
 .submit-btn { background-color: #2980b9 !important; color: white !important; }
@@ -280,74 +261,72 @@ css = """
 .canvas-output { border: 2px solid #4682B4; border-radius: 10px; padding: 20px; }
 """
 
-with gr.Blocks(css=css) as demo:
+# Create the Gradio Interface
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
     gr.Markdown("# **[Multimodal VLM Thinking with Qwen3-VL](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct)**")
-    
-    with gr.Tabs():
-        with gr.TabItem("Image Inference"):
-            with gr.Row():
-                with gr.Column():
+    with gr.Row():
+        with gr.Column():
+            with gr.Tabs():
+                # Tab 1: Image Inference
+                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", height=290)
                     image_submit = gr.Button("Submit", elem_classes="submit-btn")
-                    #gr.Examples(examples=image_examples, inputs=[image_query, image_upload])
-                with gr.Column():
-                     with gr.Column(elem_classes="canvas-output"):
-                        gr.Markdown("## Output")
-                        output_img, markdown_output_img = gr.Textbox(label="Raw Output Stream", interactive=False, lines=15, show_copy_button=True), gr.Markdown(label="(Result.Md)")
-
-        with gr.TabItem("Video Inference"):
-            with gr.Row():
-                with gr.Column():
+                    gr.Examples(examples=image_examples, inputs=[image_query, image_upload])
+                
+                # Tab 2: Video Inference
+                with gr.TabItem("Video Inference"):
                     video_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
                     video_upload = gr.Video(label="Video", height=290)
                     video_submit = gr.Button("Submit", elem_classes="submit-btn")
-                    #gr.Examples(examples=video_examples, inputs=[video_query, video_upload])
-                with gr.Column():
-                     with gr.Column(elem_classes="canvas-output"):
-                        gr.Markdown("## Output")
-                        output_vid, markdown_output_vid = gr.Textbox(label="Raw Output Stream", interactive=False, lines=15, show_copy_button=True), gr.Markdown(label="(Result.Md)")
-
-        with gr.TabItem("Object Detection & Pointing"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    detection_image_input = gr.Image(label="Upload an image", type="pil", height=400)
-                    detection_task_type = gr.Radio(choices=["Object Detection", "Point Detection"], label="Task Type", value="Object Detection")
-                    detection_prompt_input = gr.Textbox(label="Object to Detect/Point", placeholder="e.g., 'car', 'the person's face'")
-                    detection_submit_btn = gr.Button(value="🚀 Find Objects", variant="primary")
-                with gr.Column(scale=1):
-                    detection_output_image = gr.Image(type="pil", label="Result", height=400)
-                    detection_output_textbox = gr.Textbox(label="Model Raw Output (Coordinates)", lines=10, show_copy_button=True)
-            #gr.Examples(examples=detection_examples, inputs=[detection_image_input, detection_task_type, detection_prompt_input])
-
-    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)
-        
-    gr.Markdown("---")
-    gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-Thinking/discussions)")
-    gr.Markdown("> Using **[Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct)**, a powerful and versatile vision-language model. It excels at understanding and processing both text and visual information, making it suitable for a wide range of multimodal tasks.")
-    gr.Markdown("> ⚠️ Note: Video inference performance can vary depending on the complexity and length of the video.")
-
-    # Event Handlers
+                    gr.Examples(examples=video_examples, inputs=[video_query, video_upload])
+
+                # Tab 3: Object Detection
+                with gr.TabItem("Object Detection & Pointing"):
+                    detection_image_upload = gr.Image(type="pil", label="Image to Analyze", height=290)
+                    detection_query = gr.Textbox(label="Object to Detect", placeholder="e.g., car, person, cat...")
+                    detection_submit = gr.Button("Detect Objects", elem_classes="submit-btn")
+                    gr.Examples(examples=detection_examples, inputs=[detection_image_upload, detection_query])
+
+
+            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)
+
+        with gr.Column():
+            with gr.Column(elem_classes="canvas-output"):
+                gr.Markdown("## Output")
+                # Outputs for Image/Video Inference
+                output_stream = gr.Textbox(label="Raw Output Stream", interactive=False, lines=5, show_copy_button=True)
+                markdown_output = gr.Markdown(label="Formatted Output (Result.md)")
+                
+                # Outputs for Object Detection
+                annotated_image = gr.Image(type="pil", label="Annotated Image")
+                json_output = gr.JSON(label="Detection JSON Output")
+
+            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-Thinking/discussions)")
+            gr.Markdown("> Using **[Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct)**, a powerful and versatile vision-language model. It excels at understanding and processing both text and visual information, making it suitable for a wide range of multimodal tasks like visual question answering, video analysis, and object detection.")
+            gr.Markdown("> ⚠️ Note: Performance can vary depending on the complexity of the input.")
+
+    # Wire up the events
     image_submit.click(
         fn=generate_image,
         inputs=[image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
-        outputs=[output_img, markdown_output_img]
+        outputs=[output_stream, markdown_output]
     )
     video_submit.click(
         fn=generate_video,
         inputs=[video_query, video_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
-        outputs=[output_vid, markdown_output_vid]
+        outputs=[output_stream, markdown_output]
     )
-    detection_submit_btn.click(
-        fn=generate_detection_and_pointing,
-        inputs=[detection_image_input, detection_prompt_input, detection_task_type],
-        outputs=[detection_output_image, detection_output_textbox]
+    detection_submit.click(
+        fn=generate_detection,
+        inputs=[detection_image_upload, detection_query],
+        outputs=[annotated_image, json_output]
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=50).launch(mcp_server=True, ssr_mode=False, show_error=True)
+    demo.queue(max_size=50).launch(share=True, ssr_mode=False, show_error=True)