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| import gradio as gr | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| import zipfile | |
| import os | |
| # ==================== Configuration ==================== | |
| REPO_ID = "Deva8/Generative-VQA-V2-Curated" | |
| CACHE_DIR = "./dataset_cache" | |
| # Global state | |
| dataset_state = { | |
| "df": None, | |
| "images_dir": None, | |
| "loaded": False, | |
| "error": None | |
| } | |
| # ==================== Dataset Loading ==================== | |
| def initialize_dataset(): | |
| """Download and setup the dataset""" | |
| if dataset_state["loaded"]: | |
| return | |
| try: | |
| print("📥 Downloading metadata...") | |
| csv_path = hf_hub_download( | |
| repo_id=REPO_ID, | |
| filename="main_metadata.csv", | |
| repo_type="dataset", | |
| cache_dir=CACHE_DIR | |
| ) | |
| dataset_state["df"] = pd.read_csv(csv_path) | |
| print(f"✓ Loaded {len(dataset_state['df']):,} examples") | |
| print("📦 Downloading images (10GB, please wait)...") | |
| zip_path = hf_hub_download( | |
| repo_id=REPO_ID, | |
| filename="gen_vqa_v2-images.zip", | |
| repo_type="dataset", | |
| cache_dir=CACHE_DIR | |
| ) | |
| dataset_state["images_dir"] = os.path.join(CACHE_DIR, "extracted") | |
| if not os.path.exists(dataset_state["images_dir"]): | |
| print("📂 Extracting images...") | |
| os.makedirs(dataset_state["images_dir"], exist_ok=True) | |
| with zipfile.ZipFile(zip_path, 'r') as zf: | |
| zf.extractall(dataset_state["images_dir"]) | |
| dataset_state["loaded"] = True | |
| print("✅ Dataset ready!") | |
| except Exception as e: | |
| dataset_state["error"] = str(e) | |
| print(f"❌ Error: {e}") | |
| # Load dataset on startup | |
| initialize_dataset() | |
| # ==================== Helper Functions ==================== | |
| def load_image(file_path): | |
| """Load image with error handling""" | |
| try: | |
| full_path = os.path.join(dataset_state["images_dir"], file_path) | |
| return Image.open(full_path).convert('RGB') | |
| except Exception as e: | |
| print(f"Error loading image: {e}") | |
| return None | |
| def check_dataset(): | |
| """Check if dataset is loaded""" | |
| if not dataset_state["loaded"]: | |
| msg = dataset_state["error"] if dataset_state["error"] else "Dataset is loading..." | |
| return None, f"⏳ {msg}", "", "" | |
| # ==================== Main Functions ==================== | |
| def show_random(): | |
| """Display a random example""" | |
| check = check_dataset() | |
| if check: | |
| return check | |
| sample = dataset_state["df"].sample(1).iloc[0] | |
| img = load_image(sample['file_name']) | |
| return ( | |
| img, | |
| sample['question'], | |
| sample['answer'], | |
| f"Image ID: {sample['image_id']} | Question ID: {sample['question_id']}" | |
| ) | |
| def search_question(query): | |
| """Search by question keywords""" | |
| check = check_dataset() | |
| if check: | |
| return check | |
| if not query or len(query.strip()) < 2: | |
| return None, "Enter at least 2 characters", "", "" | |
| matches = dataset_state["df"][ | |
| dataset_state["df"]['question'].str.contains(query, case=False, na=False) | |
| ] | |
| if len(matches) == 0: | |
| return None, f"No matches for '{query}'", "", "" | |
| sample = matches.sample(1).iloc[0] | |
| img = load_image(sample['file_name']) | |
| return ( | |
| img, | |
| sample['question'], | |
| sample['answer'], | |
| f"Found {len(matches):,} matches | Showing random example" | |
| ) | |
| def search_answer(query): | |
| """Search by answer""" | |
| check = check_dataset() | |
| if check: | |
| return check | |
| if not query: | |
| return None, "Enter an answer", "", "" | |
| matches = dataset_state["df"][ | |
| dataset_state["df"]['answer'].str.lower() == query.lower().strip() | |
| ] | |
| if len(matches) == 0: | |
| return None, f"No examples with answer '{query}'", "", "" | |
| sample = matches.sample(1).iloc[0] | |
| img = load_image(sample['file_name']) | |
| return ( | |
| img, | |
| sample['question'], | |
| sample['answer'], | |
| f"Found {len(matches):,} examples | Showing random" | |
| ) | |
| def get_stats(): | |
| """Get dataset statistics""" | |
| if not dataset_state["loaded"]: | |
| return "Dataset loading..." | |
| df = dataset_state["df"] | |
| top_answers = df['answer'].value_counts().head(10) | |
| stats = f"""# 📊 Dataset Statistics | |
| **Total Examples:** {len(df):,} | |
| **Unique Images:** {df['image_id'].nunique():,} | |
| **Unique Answers:** {df['answer'].nunique():,} | |
| ## Top 10 Answers | |
| """ | |
| for i, (ans, count) in enumerate(top_answers.items(), 1): | |
| stats += f"{i}. **{ans}** - {count:,} examples\n" | |
| return stats | |
| # ==================== Gradio Interface ==================== | |
| with gr.Blocks( | |
| title="VQA Dataset Explorer", | |
| theme=gr.themes.Soft(primary_hue="blue") | |
| ) as demo: | |
| gr.Markdown(""" | |
| # 🎯 Generative VQA-V2 Dataset Explorer | |
| Explore 135K+ curated visual question-answer pairs from the | |
| [Generative-VQA-V2-Curated](https://huggingface.co/datasets/Deva8/Generative-VQA-V2-Curated) dataset. | |
| """) | |
| with gr.Tabs(): | |
| # Random Samples Tab | |
| with gr.TabItem("🎲 Random"): | |
| gr.Markdown("### Browse random examples") | |
| btn_random = gr.Button("🔄 Show Random Example", variant="primary", size="lg") | |
| with gr.Row(): | |
| img_random = gr.Image(label="Image", height=400) | |
| with gr.Column(): | |
| q_random = gr.Textbox(label="❓ Question", lines=3) | |
| a_random = gr.Textbox(label="✅ Answer", lines=2) | |
| m_random = gr.Textbox(label="ℹ️ Info", lines=1) | |
| btn_random.click( | |
| show_random, | |
| outputs=[img_random, q_random, a_random, m_random] | |
| ) | |
| # Question Search Tab | |
| with gr.TabItem("🔍 Search Questions"): | |
| gr.Markdown("### Find questions containing keywords") | |
| with gr.Row(): | |
| query_q = gr.Textbox( | |
| label="Search", | |
| placeholder="e.g., color, wearing, many, holding", | |
| scale=4 | |
| ) | |
| btn_q = gr.Button("🔎 Search", variant="primary", scale=1) | |
| with gr.Row(): | |
| img_q = gr.Image(label="Image", height=400) | |
| with gr.Column(): | |
| q_q = gr.Textbox(label="❓ Question", lines=3) | |
| a_q = gr.Textbox(label="✅ Answer", lines=2) | |
| m_q = gr.Textbox(label="ℹ️ Info", lines=1) | |
| btn_q.click(search_question, inputs=[query_q], outputs=[img_q, q_q, a_q, m_q]) | |
| query_q.submit(search_question, inputs=[query_q], outputs=[img_q, q_q, a_q, m_q]) | |
| # Answer Search Tab | |
| with gr.TabItem("🎯 Search Answers"): | |
| gr.Markdown("### Find examples with specific answers") | |
| with gr.Row(): | |
| query_a = gr.Textbox( | |
| label="Answer", | |
| placeholder="e.g., red, cat, pizza, 2", | |
| scale=4 | |
| ) | |
| btn_a = gr.Button("🔎 Search", variant="primary", scale=1) | |
| gr.Markdown("**Popular:** white, black, blue, red, 2, 3, dog, cat, pizza") | |
| with gr.Row(): | |
| img_a = gr.Image(label="Image", height=400) | |
| with gr.Column(): | |
| q_a = gr.Textbox(label="❓ Question", lines=3) | |
| a_a = gr.Textbox(label="✅ Answer", lines=2) | |
| m_a = gr.Textbox(label="ℹ️ Info", lines=1) | |
| btn_a.click(search_answer, inputs=[query_a], outputs=[img_a, q_a, a_a, m_a]) | |
| query_a.submit(search_answer, inputs=[query_a], outputs=[img_a, q_a, a_a, m_a]) | |
| # Statistics Tab | |
| with gr.TabItem("📊 Stats"): | |
| gr.Markdown("### Dataset overview and analysis") | |
| btn_stats = gr.Button("📈 Load Statistics", variant="primary") | |
| stats_md = gr.Markdown() | |
| btn_stats.click(get_stats, outputs=[stats_md]) | |
| gr.Markdown(""" | |
| --- | |
| ### About | |
| This dataset is a curated version of VQA v2 with: | |
| - ✅ No yes/no questions | |
| - ✅ Balanced answer distribution | |
| - ✅ Filtered ambiguous questions | |
| **Dataset:** [Deva8/Generative-VQA-V2-Curated](https://huggingface.co/datasets/Deva8/Generative-VQA-V2-Curated) | |
| **License:** CC BY 4.0 | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860) |