Upload app.py

app.py

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+import os
+import time
+import threading
+import gradio as gr
+import spaces
+import torch
+import numpy as np
+from PIL import Image
+import cv2
+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_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,
+    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 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):
+    """
+    Generate responses using the selected model for image 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 image is None:
+        yield "Please upload an image.", "Please upload an image."
+        return
+
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": text},
+        ]
+    }]
+    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=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}
+    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,
+    }
+    thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        buffer = buffer.replace("<|im_end|>", "")
+        time.sleep(0.01)
+        yield buffer, buffer
+
+# 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"]
+]
+
+# Updated CSS with model choice highlighting
+css = """
+.submit-btn {
+    background-color: #2980b9 !important;
+    color: white !important;
+}
+.submit-btn:hover {
+    background-color: #3498db !important;
+}
+.canvas-output {
+    border: 2px solid #4682B4;
+    border-radius: 10px;
+    padding: 20px;
+}
+"""
+
+# 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)**")
+    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]
+                    )
+            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")
+                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=3)
+                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"
+            )
+            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])
+
+if __name__ == "__main__":
+    demo.queue(max_size=40).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)