update app

app.py

@@ -5,7 +5,9 @@ import json
 import time
 import asyncio
 from threading import Thread
-from typing import Iterable
+from pathlib import Path
+from io import BytesIO
+from typing import Optional, Tuple, Dict, Any, Iterable
 
 import gradio as gr
 import spaces
@@ -13,40 +15,44 @@ import torch
 import numpy as np
 from PIL import Image
 import cv2
+import requests
+import fitz
 
 from transformers import (
     Qwen2_5_VLForConditionalGeneration,
     Qwen3VLForConditionalGeneration,
-    AutoTokenizer,
     AutoProcessor,
     TextIteratorStreamer,
 )
 from transformers.image_utils import load_image
+
 from gradio.themes import Soft
 from gradio.themes.utils import colors, fonts, sizes
 
-colors.steel_blue = colors.Color(
-    name="steel_blue",
-    c50="#EBF3F8",
-    c100="#D3E5F0",
-    c200="#A8CCE1",
-    c300="#7DB3D2",
-    c400="#529AC3",
-    c500="#4682B4",  # SteelBlue base color
-    c600="#3E72A0",
-    c700="#36638C",
-    c800="#2E5378",
-    c900="#264364",
-    c950="#1E3450",
-)
+# --- Theme and CSS Definition ---
 
+# Define the new OrangeRed color palette
+colors.orange_red = colors.Color(
+    name="orange_red",
+    c50="#FFF0E5",
+    c100="#FFE0CC",
+    c200="#FFC299",
+    c300="#FFA366",
+    c400="#FF8533",
+    c500="#FF4500",  # OrangeRed base color
+    c600="#E63E00",
+    c700="#CC3700",
+    c800="#B33000",
+    c900="#992900",
+    c950="#802200",
+)
 
-class SteelBlueTheme(Soft):
+class OrangeRedTheme(Soft):
     def __init__(
         self,
         *,
         primary_hue: colors.Color | str = colors.gray,
-        secondary_hue: colors.Color | str = colors.steel_blue,
+        secondary_hue: colors.Color | str = colors.orange_red, # Use the new color
         neutral_hue: colors.Color | str = colors.slate,
         text_size: sizes.Size | str = sizes.text_lg,
         font: fonts.Font | str | Iterable[fonts.Font | str] = (
@@ -73,8 +79,8 @@ class SteelBlueTheme(Soft):
             button_primary_text_color_hover="white",
             button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
             button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
-            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_800)",
-            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_500)",
+            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)",
+            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
             button_secondary_text_color="black",
             button_secondary_text_color_hover="white",
             button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
@@ -92,51 +98,104 @@ class SteelBlueTheme(Soft):
             block_label_background_fill="*primary_200",
         )
 
-steel_blue_theme = SteelBlueTheme()
+# Instantiate the new theme
+orange_red_theme = OrangeRedTheme()
+
+css = """
+#main-title h1 {
+    font-size: 2.3em !important;
+}
+#output-title h2 {
+    font-size: 2.1em !important;
+}
+"""
 
 MAX_MAX_NEW_TOKENS = 4096
 DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device:", device)
 
-# Load Qwen2.5-VL-7B-Instruct
-MODEL_ID_M = "Qwen/Qwen2.5-VL-7B-Instruct"
-processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
-model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_M,
+# --- Model Loading ---
+
+# Load Qwen3-VL-4B-Instruct
+MODEL_ID_Q4B = "Qwen/Qwen3-VL-4B-Instruct"
+processor_q4b = AutoProcessor.from_pretrained(MODEL_ID_Q4B, trust_remote_code=True)
+model_q4b = Qwen3VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_Q4B,
     trust_remote_code=True,
-    torch_dtype=torch.float16
+    torch_dtype=torch.bfloat16
 ).to(device).eval()
 
-# Load Qwen2.5-VL-3B-Instruct
-MODEL_ID_X = "Qwen/Qwen2.5-VL-3B-Instruct"
-processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
-model_x = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_X,
+# Load Qwen3-VL-8B-Instruct
+MODEL_ID_Q8B = "Qwen/Qwen3-VL-8B-Instruct"
+processor_q8b = AutoProcessor.from_pretrained(MODEL_ID_Q8B, trust_remote_code=True)
+model_q8b = Qwen3VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_Q8B,
     trust_remote_code=True,
-    torch_dtype=torch.float16
+    torch_dtype=torch.bfloat16
 ).to(device).eval()
 
 # Load Qwen3-VL-2B-Instruct
-MODEL_ID_Q = "Qwen/Qwen3-VL-2B-Instruct"
-processor_q = AutoProcessor.from_pretrained(MODEL_ID_Q, trust_remote_code=True)
-model_q = Qwen3VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_Q,
+MODEL_ID_Q2B = "Qwen/Qwen3-VL-2B-Instruct"
+processor_q2b = AutoProcessor.from_pretrained(MODEL_ID_Q2B, trust_remote_code=True)
+model_q2b = Qwen3VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_Q2B,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16
+).to(device).eval()
+
+# Load Qwen2.5-VL-7B-Instruct
+MODEL_ID_M7B = "Qwen/Qwen2.5-VL-7B-Instruct"
+processor_m7b = AutoProcessor.from_pretrained(MODEL_ID_M7B, trust_remote_code=True)
+model_m7b = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_M7B,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+# Load Qwen2.5-VL-3B-Instruct
+MODEL_ID_X3B = "Qwen/Qwen2.5-VL-3B-Instruct"
+processor_x3b = AutoProcessor.from_pretrained(MODEL_ID_X3B, trust_remote_code=True)
+model_x3b = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_X3B,
     trust_remote_code=True,
     torch_dtype=torch.float16
 ).to(device).eval()
 
+
+# --- Helper Functions ---
+
+def select_model(model_name: str):
+    if model_name == "Qwen3-VL-4B-Instruct":
+        return processor_q4b, model_q4b
+    elif model_name == "Qwen3-VL-8B-Instruct":
+        return processor_q8b, model_q8b
+    elif model_name == "Qwen3-VL-2B-Instruct":
+        return processor_q2b, model_q2b
+    elif model_name == "Qwen2.5-VL-7B-Instruct":
+        return processor_m7b, model_m7b
+    elif model_name == "Qwen2.5-VL-3B-Instruct":
+        return processor_x3b, model_x3b
+    else:
+        raise ValueError("Invalid model selected.")
+
+def extract_gif_frames(gif_path: str):
+    if not gif_path:
+        return []
+    with Image.open(gif_path) as gif:
+        total_frames = gif.n_frames
+        frame_indices = np.linspace(0, total_frames - 1, min(total_frames, 10), dtype=int)
+        frames = []
+        for i in frame_indices:
+            gif.seek(i)
+            frames.append(gif.convert("RGB").copy())
+    return frames
+
 def downsample_video(video_path):
-    """
-    Downsamples the video to evenly spaced frames.
-    Each frame is returned as a PIL image along 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 = []
-    # Use a maximum of 10 frames to avoid excessive memory usage
     frame_indices = np.linspace(0, total_frames - 1, min(total_frames, 10), dtype=int)
     for i in frame_indices:
         vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
@@ -144,39 +203,75 @@ def downsample_video(video_path):
         if success:
             image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
             pil_image = Image.fromarray(image)
-            timestamp = round(i / fps, 2)
-            frames.append((pil_image, timestamp))
+            frames.append(pil_image)
     vidcap.release()
     return frames
 
-@spaces.GPU
-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):
-    """
-    Generates responses using the selected model for image input.
-    """
-    if model_name == "Qwen2.5-VL-7B-Instruct":
-        processor, model = processor_m, model_m
-    elif model_name == "Qwen2.5-VL-3B-Instruct":
-        processor, model = processor_x, model_x
-    elif model_name == "Qwen3-VL-2B-Instruct":
-        processor, model = processor_q, model_q
+def convert_pdf_to_images(file_path: str, dpi: int = 200):
+    if not file_path:
+        return []
+    images = []
+    pdf_document = fitz.open(file_path)
+    zoom = dpi / 72.0
+    mat = fitz.Matrix(zoom, zoom)
+    for page_num in range(len(pdf_document)):
+        page = pdf_document.load_page(page_num)
+        pix = page.get_pixmap(matrix=mat)
+        img_data = pix.tobytes("png")
+        images.append(Image.open(BytesIO(img_data)))
+    pdf_document.close()
+    return images
+
+def get_initial_pdf_state() -> Dict[str, Any]:
+    return {"pages": [], "total_pages": 0, "current_page_index": 0}
+
+def load_and_preview_pdf(file_path: Optional[str]) -> Tuple[Optional[Image.Image], Dict[str, Any], str]:
+    state = get_initial_pdf_state()
+    if not file_path:
+        return None, state, '<div style="text-align:center;">No file loaded</div>'
+    try:
+        pages = convert_pdf_to_images(file_path)
+        if not pages:
+            return None, state, '<div style="text-align:center;">Could not load file</div>'
+        state["pages"] = pages
+        state["total_pages"] = len(pages)
+        page_info_html = f'<div style="text-align:center;">Page 1 / {state["total_pages"]}</div>'
+        return pages[0], state, page_info_html
+    except Exception as e:
+        return None, state, f'<div style="text-align:center;">Failed to load preview: {e}</div>'
+
+def navigate_pdf_page(direction: str, state: Dict[str, Any]):
+    if not state or not state["pages"]:
+        return None, state, '<div style="text-align:center;">No file loaded</div>'
+    current_index = state["current_page_index"]
+    total_pages = state["total_pages"]
+    if direction == "prev":
+        new_index = max(0, current_index - 1)
+    elif direction == "next":
+        new_index = min(total_pages - 1, current_index + 1)
     else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
+        new_index = current_index
+    state["current_page_index"] = new_index
+    image_preview = state["pages"][new_index]
+    page_info_html = f'<div style="text-align:center;">Page {new_index + 1} / {total_pages}</div>'
+    return image_preview, state, page_info_html
 
+# --- Generation Functions ---
+
+@spaces.GPU
+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):
     if image is None:
         yield "Please upload an image.", "Please upload an image."
         return
-
+    try:
+        processor, model = select_model(model_name)
+    except ValueError as e:
+        yield str(e), str(e)
+        return
+        
     messages = [{"role": "user", "content": [{"type": "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).to(device)
+    inputs = processor(text=[prompt_full], images=[image], return_tensors="pt", padding=True).to(device)
     streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
     generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
     thread = Thread(target=model.generate, kwargs=generation_kwargs)
@@ -188,86 +283,143 @@ def generate_image(model_name: str, text: str, image: Image.Image,
         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):
-    """
-    Generates responses using the selected model for video input.
-    """
-    if model_name == "Qwen2.5-VL-7B-Instruct":
-        processor, model = processor_m, model_m
-    elif model_name == "Qwen2.5-VL-3B-Instruct":
-        processor, model = processor_x, model_x
-    elif model_name == "Qwen3-VL-2B-Instruct":
-        processor, model = processor_q, model_q
-    else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
-
+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):
     if video_path is None:
         yield "Please upload a video.", "Please upload a video."
         return
+    try:
+        processor, model = select_model(model_name)
+    except ValueError as e:
+        yield str(e), str(e)
+        return
 
-    frames_with_ts = downsample_video(video_path)
-    if not frames_with_ts:
+    frames = downsample_video(video_path)
+    if not frames:
         yield "Could not process video.", "Could not process video."
         return
-
+        
     messages = [{"role": "user", "content": [{"type": "text", "text": text}]}]
-    images_for_processor = []
-    for frame, timestamp in frames_with_ts:
-        messages[0]["content"].append({"type": "image"})
-        images_for_processor.append(frame)
+    for frame in frames:
+        messages[0]["content"].insert(0, {"type": "image"})
+    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(text=[prompt_full], images=frames, return_tensors="pt", padding=True).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 = 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
+
+@spaces.GPU
+def generate_pdf(model_name: str, text: str, state: Dict[str, Any], max_new_tokens: int = 2048, temperature: float = 0.6, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.2):
+    if not state or not state["pages"]:
+        yield "Please upload a PDF file first.", "Please upload a PDF file first."
+        return
+    try:
+        processor, model = select_model(model_name)
+    except ValueError as e:
+        yield str(e), str(e)
+        return
 
+    page_images = state["pages"]
+    full_response = ""
+    for i, image in enumerate(page_images):
+        page_header = f"--- Page {i+1}/{len(page_images)} ---\n"
+        yield full_response + page_header, full_response + page_header
+        messages = [{"role": "user", "content": [{"type": "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).to(device)
+        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+        generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
+        thread = Thread(target=model.generate, kwargs=generation_kwargs)
+        thread.start()
+        page_buffer = ""
+        for new_text in streamer:
+            page_buffer += new_text
+            yield full_response + page_header + page_buffer, full_response + page_header + page_buffer
+            time.sleep(0.01)
+        full_response += page_header + page_buffer + "\n\n"
+
+@spaces.GPU
+def generate_caption(model_name: 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):
+    if image is None:
+        yield "Please upload an image to caption.", "Please upload an image to caption."
+        return
+    try:
+        processor, model = select_model(model_name)
+    except ValueError as e:
+        yield str(e), str(e)
+        return
+        
+    system_prompt = (
+        "You are an AI assistant. For the given image, write a precise caption and provide a structured set of "
+        "attributes describing visual elements like objects, people, actions, colors, and environment."
+    )
+    messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": system_prompt}]}]
     prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    inputs = processor(
-        text=[prompt_full], images=images_for_processor, return_tensors="pt", padding=True).to(device)
+    inputs = processor(text=[prompt_full], images=[image], return_tensors="pt", padding=True).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,
-    }
+    generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
     thread = 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
 
+@spaces.GPU
+def generate_gif(model_name: str, text: str, gif_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):
+    if gif_path is None:
+        yield "Please upload a GIF.", "Please upload a GIF."
+        return
+    try:
+        processor, model = select_model(model_name)
+    except ValueError as e:
+        yield str(e), str(e)
+        return
 
-# Define examples for image and video 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"],
-]
+    frames = extract_gif_frames(gif_path)
+    if not frames:
+        yield "Could not process GIF.", "Could not process GIF."
+        return
+    messages = [{"role": "user", "content": [{"type": "text", "text": text}]}]
+    for frame in frames:
+        messages[0]["content"].insert(0, {"type": "image"})
+    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(text=[prompt_full], images=frames, return_tensors="pt", padding=True).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 = 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
 
-video_examples = [
-    ["Explain the ad in detail", "videos/1.mp4"],
-    ["Identify the main actions in the video", "videos/2.mp4"],
-]
+# --- Examples and Gradio UI ---
 
-css = """
-#main-title h1 {
-    font-size: 2.3em !important;
-}
-#output-title h2 {
-    font-size: 2.1em !important;
-}
-"""
+image_examples = [["Perform OCR on the image...", "examples/images/1.jpg"],
+                  ["Caption the image. Describe the safety measures shown in the image. Conclude whether the situation is (safe or unsafe)...", "examples/images/2.jpg"],
+                  ["Solve the problem...", "examples/images/3.png"]]
+video_examples = [["Explain the Ad video in detail.", "examples/videos/1.mp4"],
+                  ["Explain the video in detail.", "examples/videos/2.mp4"]]
+pdf_examples = [["Extract the content precisely.", "examples/pdfs/doc1.pdf"],
+                ["Analyze and provide a short report.", "examples/pdfs/doc2.pdf"]]
+gif_examples = [["Describe this GIF.", "examples/gifs/1.gif"],
+                ["Describe this GIF.", "examples/gifs/2.gif"]]
+caption_examples = [["examples/captions/1.JPG"],
+                    ["examples/captions/2.jpeg"], ["examples/captions/3.jpeg"]]
 
-# Create the Gradio Interface
-with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
-    gr.Markdown("# **Qwen3-VL-Outpost**", elem_id="main-title")
+with gr.Blocks(theme=orange_red_theme, css=css) as demo:
+    pdf_state = gr.State(value=get_initial_pdf_state())
+    gr.Markdown("# **Qwen-VL: Multimodal Outpost**", elem_id="main-title")
     with gr.Row():
         with gr.Column(scale=2):
             with gr.Tabs():
@@ -276,42 +428,91 @@ with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
                     image_upload = gr.Image(type="pil", label="Upload Image", height=290)
                     image_submit = gr.Button("Submit", variant="primary")
                     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="Upload Video", height=290)
+                    video_upload = gr.Video(label="Upload Video(≤30s)", height=290)
                     video_submit = gr.Button("Submit", variant="primary")
                     gr.Examples(examples=video_examples, inputs=[video_query, video_upload])
-                    
+
+                with gr.TabItem("PDF Inference"):
+                    with gr.Row():
+                        with gr.Column(scale=1):
+                            pdf_query = gr.Textbox(label="Query Input", placeholder="e.g., 'Summarize this document'")
+                            pdf_upload = gr.File(label="Upload PDF", file_types=[".pdf"])
+                            pdf_submit = gr.Button("Submit", variant="primary")
+                        with gr.Column(scale=1):
+                            pdf_preview_img = gr.Image(label="PDF Preview", height=290)
+                            with gr.Row():
+                                prev_page_btn = gr.Button("◀ Previous")
+                                page_info = gr.HTML('<div style="text-align:center;">No file loaded</div>')
+                                next_page_btn = gr.Button("Next ▶")
+                    gr.Examples(examples=pdf_examples, inputs=[pdf_query, pdf_upload])
+
+                with gr.TabItem("Gif Inference"):
+                    gif_query = gr.Textbox(label="Query Input", placeholder="e.g., 'What is happening in this gif?'")
+                    gif_upload = gr.Image(type="filepath", label="Upload GIF", height=290)
+                    gif_submit = gr.Button("Submit", variant="primary")
+                    gr.Examples(examples=gif_examples, inputs=[gif_query, gif_upload])
+
+                with gr.TabItem("Caption"):
+                    caption_image_upload = gr.Image(type="pil", label="Image to Caption", height=290)
+                    caption_submit = gr.Button("Generate Caption", variant="primary")
+                    gr.Examples(examples=caption_examples, inputs=[caption_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)
-                
+
         with gr.Column(scale=3):
             gr.Markdown("## Output", elem_id="output-title")
-            output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=11, show_copy_button=True)
+            output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=14, show_copy_button=True)
             with gr.Accordion("(Result.md)", open=False):
-                markdown_output = gr.Markdown()
-                
+                markdown_output = gr.Markdown(label="(Result.Md)", latex_delimiters=[
+                                {"left": "$$", "right": "$$", "display": True},
+                                {"left": "$", "right": "$", "display": False}
+                            ])
+
             model_choice = gr.Radio(
-                choices=["Qwen3-VL-2B-Instruct", "Qwen2.5-VL-3B-Instruct", "Qwen2.5-VL-7B-Instruct"],
+                choices=[
+                    "Qwen3-VL-4B-Instruct",
+                    "Qwen3-VL-8B-Instruct",
+                    "Qwen3-VL-2B-Instruct",
+                    "Qwen2.5-VL-7B-Instruct",
+                    "Qwen2.5-VL-3B-Instruct"
+                ],
                 label="Select Model",
-                value="Qwen3-VL-2B-Instruct"
+                value="Qwen3-VL-4B-Instruct"
             )
 
-    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]
-    )
+    # --- Event Handlers ---
+    
+    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])
+    
+    pdf_submit.click(fn=generate_pdf,
+                     inputs=[model_choice, pdf_query, pdf_state, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+                     outputs=[output, markdown_output])
+                     
+    gif_submit.click(fn=generate_gif,
+                     inputs=[model_choice, gif_query, gif_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+                     outputs=[output, markdown_output])
+                     
+    caption_submit.click(fn=generate_caption,
+                         inputs=[model_choice, caption_image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+                         outputs=[output, markdown_output])
+
+    pdf_upload.change(fn=load_and_preview_pdf, inputs=[pdf_upload], outputs=[pdf_preview_img, pdf_state, page_info])
+    prev_page_btn.click(fn=lambda s: navigate_pdf_page("prev", s), inputs=[pdf_state], outputs=[pdf_preview_img, pdf_state, page_info])
+    next_page_btn.click(fn=lambda s: navigate_pdf_page("next", s), inputs=[pdf_state], outputs=[pdf_preview_img, pdf_state, page_info])
 
 if __name__ == "__main__":
     demo.queue(max_size=50).launch(mcp_server=True, ssr_mode=False, show_error=True)