Delete app.py

app.py

@@ -1,380 +0,0 @@
-import spaces
-import json
-import math
-import os
-import traceback
-from io import BytesIO
-from typing import Any, Dict, List, Optional, Tuple
-import re
-import time
-from threading import Thread
-from io import BytesIO
-import uuid
-import tempfile
-
-import gradio as gr
-import numpy as np
-import torch
-from PIL import Image
-import supervision as sv
-
-
-from transformers import (
-    Qwen2_5_VLForConditionalGeneration,
-    Glm4vForConditionalGeneration,
-    Qwen2VLForConditionalGeneration,
-    AutoModelForCausalLM,
-    AutoProcessor,
-    TextIteratorStreamer,
-)
-
-# --- Constants and Model Setup ---
-MAX_INPUT_TOKEN_LENGTH = 4096
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-print("--- System Information ---")
-print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
-print("torch.__version__ =", torch.__version__)
-print("torch.version.cuda =", torch.version.cuda)
-print("CUDA available:", torch.cuda.is_available())
-print("CUDA device count:", torch.cuda.device_count())
-if torch.cuda.is_available():
-    print("Current device:", torch.cuda.current_device())
-    print("Device name:", torch.cuda.get_device_name(torch.cuda.current_device()))
-print("Using device:", device)
-print("--------------------------")
-
-
-# --- Model Loading ---
-
-# Load Camel-Doc-OCR-062825
-print("Loading 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()
-print("Camel-Doc-OCR-062825 loaded.")
-
-# GLM-4.1V-9B-Thinking
-print("Loading GLM-4.1V-9B-Thinking")
-MODEL_ID_T = "zai-org/GLM-4.1V-9B-Thinking"
-processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
-model_t = Glm4vForConditionalGeneration.from_pretrained(
-    MODEL_ID_T,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-print("GLM-4.1V-9B-Thinking loaded.")
-
-# Load moondream3
-print("Loading moondream3-preview...")
-MODEL_ID_MD3 = "moondream/moondream3-preview"
-model_md3 = AutoModelForCausalLM.from_pretrained(
-    MODEL_ID_MD3,
-    trust_remote_code=True,
-    torch_dtype=torch.bfloat16,
-    device_map={"": "cuda"},
-)
-model_md3.compile()
-print("moondream3-preview loaded and compiled.")
-
-
-# --- Moondream3 Utility Functions ---
-
-def create_annotated_image(image, detection_result, object_name="Object"):
-    if not isinstance(detection_result, dict) or "objects" not in detection_result:
-        return image
-    
-    original_width, original_height = image.size
-    annotated_image = np.array(image.convert("RGB"))
-  
-    bboxes = []
-    labels = []
-    
-    for i, obj in enumerate(detection_result["objects"]):
-        x_min = int(obj["x_min"] * original_width)
-        y_min = int(obj["y_min"] * original_height)
-        x_max = int(obj["x_max"] * original_width)
-        y_max = int(obj["y_max"] * original_height)
-        
-        x_min = max(0, min(x_min, original_width))
-        y_min = max(0, min(y_min, original_height))
-        x_max = max(0, min(x_max, original_width))
-        y_max = max(0, min(y_max, original_height))
-        
-        if x_max > x_min and y_max > y_min:
-            bboxes.append([x_min, y_min, x_max, y_max])
-            labels.append(f"{object_name} {i+1}")
-    
-    if not bboxes:
-        return image
-        
-    detections = sv.Detections(
-        xyxy=np.array(bboxes, dtype=np.float32),
-        class_id=np.arange(len(bboxes))
-    )
-    
-    bounding_box_annotator = sv.BoxAnnotator(
-        thickness=3,
-        color_lookup=sv.ColorLookup.INDEX
-    )
-    label_annotator = sv.LabelAnnotator(
-        text_thickness=2,
-        text_scale=0.6,
-        color_lookup=sv.ColorLookup.INDEX
-    )
-    
-    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)
-
-def create_point_annotated_image(image, point_result):
-    if not isinstance(point_result, dict) or "points" not in point_result:
-        return image
-    
-    original_width, original_height = image.size
-    annotated_image = np.array(image.convert("RGB"))
-    
-    points = []
-    for point in point_result["points"]:
-        x = int(point["x"] * original_width)
-        y = int(point["y"] * original_height)
-        points.append([x, y])
-    
-    if points:
-        points_array = np.array(points).reshape(1, -1, 2)
-        key_points = sv.KeyPoints(xy=points_array)
-        vertex_annotator = sv.VertexAnnotator(radius=8, color=sv.Color.RED)
-        annotated_image = vertex_annotator.annotate(
-            scene=annotated_image, key_points=key_points
-        )
-    
-    return Image.fromarray(annotated_image)
-
-@spaces.GPU()
-def detect_objects_md3(image, prompt, task_type, max_objects):
-    STANDARD_SIZE = (1024, 1024)
-    if image is None:
-        raise gr.Error("Please upload an image.")
-    image.thumbnail(STANDARD_SIZE)
-    
-    t0 = time.perf_counter()
-    
-    if task_type == "Object Detection":
-        settings = {"max_objects": max_objects} if max_objects > 0 else {}
-        result = model_md3.detect(image, prompt, settings=settings)
-        annotated_image = create_annotated_image(image, result, prompt)
-    elif task_type == "Point Detection":
-        result = model_md3.point(image, prompt)
-        annotated_image = create_point_annotated_image(image, result)
-    elif task_type == "Caption":
-        result = model_md3.caption(image, length="normal")
-        annotated_image = image  
-    else:  
-        result = model_md3.query(image=image, question=prompt, reasoning=True)
-        annotated_image = image  
-          
-    elapsed_ms = (time.perf_counter() - t0) * 1_000
-    
-    if isinstance(result, dict):
-        if "objects" in result:
-          output_text = f"Found {len(result['objects'])} objects:\n"
-          for i, obj in enumerate(result['objects'], 1):
-              output_text += f"\n{i}. Bounding box: ({obj['x_min']:.3f}, {obj['y_min']:.3f}, {obj['x_max']:.3f}, {obj['y_max']:.3f})"
-        elif "points" in result:
-            output_text = f"Found {len(result['points'])} points:\n"
-            for i, point in enumerate(result['points'], 1):
-                output_text += f"\n{i}. Point: ({point['x']:.3f}, {point['y']:.3f})"
-        elif "caption" in result:
-            output_text = result['caption']
-        elif "answer" in result:
-            output_text = f"Reasoning: {result.get('reasoning', 'N/A')}\n\nAnswer: {result['answer']}"
-        else:
-            output_text = json.dumps(result, indent=2)
-    else:
-        output_text = str(result)
-    
-    timing_text = f"Inference time: {elapsed_ms:.0f} ms"
-    
-    return annotated_image, output_text, timing_text
-
-
-# --- Core Application Logic (for other models) ---
-@spaces.GPU
-def process_document_stream(
-    model_name: str, 
-    image: Image.Image, 
-    prompt_input: str, 
-    max_new_tokens: int,
-    temperature: float,
-    top_p: float,
-    top_k: int,
-    repetition_penalty: float
-):
-    """
-    Main generator function for models other than Moondream3.
-    """
-    if image is None:
-        yield "Please upload an image."
-        return
-    if not prompt_input or not prompt_input.strip():
-        yield "Please enter a prompt."
-        return
-
-    # Select processor and model based on dropdown choice
-    if model_name == "Camel-Doc-OCR-062825 (OCR)":
-        processor, model = processor_m, model_m
-    elif model_name == "GLM-4.1V-9B (Thinking)":
-        processor, model = processor_t, model_t
-    else:
-        yield "Invalid model selected."
-        return
-            
-    messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt_input}]}]
-    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    inputs = processor(text=[prompt_full], images=[image], return_tensors="pt", padding=True, truncation=True, 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,
-        "temperature": temperature,
-        "top_p": top_p,
-        "top_k": top_k,
-        "repetition_penalty": repetition_penalty,
-        "do_sample": True if temperature > 0 else False
-    }
-
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        # Clean up potential model-specific tokens
-        buffer = buffer.replace("<|im_end|>", "").replace("</s>", "")
-        time.sleep(0.01)
-        yield buffer
-
-# --- Gradio UI Definition ---
-def create_gradio_interface():
-    """Builds and returns the Gradio web interface."""
-    css = """
-    .main-container { max-width: 1400px; margin: 0 auto; }
-    .process-button { border: none !important; color: white !important; font-weight: bold !important; background-color: blue !important;}
-    .process-button:hover { background-color: darkblue !important; transform: translateY(-2px) !important; box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important; }
-    .processr-button { border: none !important; color: white !important; font-weight: bold !important; background-color: blue !important;}
-    .processr-button:hover { background-color: darkblue !important; transform: translateY(-2px) !important; box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important; }
-    """
-    with gr.Blocks(theme="bethecloud/storj_theme", css=css) as demo:
-        gr.Markdown("# Multimodal VLM v1.0 ⚡")
-        gr.Markdown("Explore the capabilities of various Vision Language Models for tasks like OCR, VQA, and Object Detection.")
-
-        with gr.Tabs():
-            # --- TAB 1: Document and General VLMs ---
-            with gr.TabItem("📄 Document & General VLM"):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        #gr.Markdown("### 1. Configure Inputs")
-                        model_choice = gr.Dropdown(
-                            choices=["Camel-Doc-OCR-062825 (OCR)", "GLM-4.1V-9B (Thinking)"],
-                            label="Select Model", value= "Camel-Doc-OCR-062825 (OCR)"
-                        )
-                        image_input_doc = gr.Image(label="Upload Image", type="pil", sources=['upload'], height=280)
-                        prompt_input_doc = gr.Textbox(label="Query Input", placeholder="e.g., 'Transcribe the text in this document.'")
-                        
-                        with gr.Accordion("Advanced Generation Settings", open=False):
-                            max_new_tokens = gr.Slider(minimum=256, maximum=4096, value=2048, step=128, label="Max New Tokens")
-                            temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=2.0, step=0.1, value=0.7)
-                            top_p = gr.Slider(label="Top-p", minimum=0.1, maximum=1.0, step=0.05, value=0.9)
-                            top_k = gr.Slider(label="Top-k", minimum=1, maximum=100, step=1, value=40)
-                            repetition_penalty = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.1)
-
-                        process_btn = gr.Button("🚀 Process", variant="primary", elem_classes=["process-button"])
-                        clear_btn = gr.Button("🗑️ Clear", variant="secondary")
-
-                    with gr.Column(scale=2):
-                        #gr.Markdown("### 2. View Output")
-                        with gr.Tab("Output Stream"):
-                            output_stream = gr.Textbox(label="Model Output", interactive=False, lines=24, show_copy_button=True)
-                            
-                gr.Examples(
-                    examples=[
-                        ["examples/1.jpg", "Transcribe this receipt."],
-                        ["examples/2.jpg", "Extract the content."],
-                        ["examples/3.jpg", "OCR the image."],
-                    ],
-                    inputs=[image_input_doc, prompt_input_doc]
-                )
-            
-            # --- TAB 2: Moondream3 Lab ---
-            with gr.TabItem("🌝 Moondream3"):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        md3_image_input = gr.Image(label="Upload an image", type="pil", height=400)
-                        md3_task_type = gr.Radio(
-                            choices=["Object Detection", "Point Detection", "Caption", "Visual Question Answering"],
-                            label="Task Type", value="Object Detection"
-                        )
-                        md3_prompt_input = gr.Textbox(
-                            label="Prompt (object to detect/question to ask)",
-                            placeholder="e.g., 'car', 'person', 'What's in this image?'"
-                        )
-                        md3_max_objects = gr.Number(
-                            label="Max Objects (for Object Detection only)",
-                            value=10, minimum=1, maximum=50, step=1, visible=True
-                        )
-                        md3_generate_btn = gr.Button(value="🚀 Process", variant="primary", elem_classes=["processr-button"])
-                    with gr.Column(scale=1):
-                        md3_output_image = gr.Image(type="pil", label="Result", height=400)
-                        md3_output_textbox = gr.Textbox(label="Model Response", lines=10, show_copy_button=True)
-                        md3_output_time = gr.Markdown()
-
-                gr.Examples(
-                    examples=[
-                        ["md3/1.jpg", "Object Detection", "boats", 7],
-                        ["md3/2.jpg", "Point Detection", "candy", 7],
-                        ["md3/3.png", "Caption", "", 5],
-                        ["md3/4.jpeg", "Visual Question Answering", "Analyze the GDP trend over the yearsAnalyze the GDP trend over the years.", 5],
-                    ],
-                    inputs=[md3_image_input, md3_task_type, md3_prompt_input, md3_max_objects],
-                    label="Click an example to populate inputs"
-                )
-
-        # --- Event Handlers ---
-        
-        # Document Tab
-        process_btn.click(
-            fn=process_document_stream,
-            inputs=[model_choice, image_input_doc, prompt_input_doc, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
-            outputs=[output_stream]
-        )
-        clear_btn.click(lambda: (None, "", ""), outputs=[image_input_doc, prompt_input_doc, output_stream])
-
-        # Moondream3 Tab
-        def update_max_objects_visibility(task):
-            return gr.update(visible=(task == "Object Detection"))
-        
-        md3_task_type.change(fn=update_max_objects_visibility, inputs=[md3_task_type], outputs=[md3_max_objects])
-        
-        md3_generate_btn.click(
-            fn=detect_objects_md3,
-            inputs=[md3_image_input, md3_prompt_input, md3_task_type, md3_max_objects],
-            outputs=[md3_output_image, md3_output_textbox, md3_output_time]
-        )
-        
-    return demo
-
-if __name__ == "__main__":
-    demo = create_gradio_interface()
-    demo.queue(max_size=50).launch(share=True, show_error=True)