Add submission portal and task scripts for MediaEval Medico 2025 challenge

medvqa/competitions/medico-2025/__init__.py

@@ -0,0 +1 @@
+# This file can be empty or contain package initialization code

medvqa/competitions/medico-2025/submission_portal.py

@@ -0,0 +1,186 @@
+import sys
+import subprocess
+import gradio as gr
+import json
+from datetime import datetime, timezone
+from huggingface_hub import upload_file, snapshot_download
+import shutil
+import os
+import glob
+from pathlib import Path
+from huggingface_hub import whoami
+import platform
+
+print(subprocess.check_output(
+    [sys.executable, "-m", "pip", "list"]).decode("utf-8"))
+print({
+    "python": platform.python_version(),
+    "os": platform.system(),
+    "platform": platform.platform(),
+    "arch": platform.machine()
+})
+print("Account token used to connect to HuggingFace: ", whoami()['name'])
+
+
+SUBMISSION_REPO = "SimulaMet/medico-2025-submissions"
+hub_path = None
+submissions = None
+last_submission_update_time = datetime.now(timezone.utc)
+
+
+def refresh_submissions():
+    global hub_path, submissions, last_submission_update_time
+    if hub_path and Path(hub_path).exists():
+        shutil.rmtree(hub_path, ignore_errors=True)
+        print("Deleted existing submissions")
+
+    hub_path = snapshot_download(
+        repo_type="dataset", repo_id=SUBMISSION_REPO, allow_patterns=['**/*.json'])
+    print("Downloaded submissions to:", hub_path)
+    if not os.path.exists(hub_path):
+        os.makedirs(hub_path)
+
+    all_jsons = glob.glob(hub_path + "/**/*.json", recursive=True)
+    print("json_files count:", len(all_jsons))
+
+    submissions = []
+    for file in all_jsons:
+        file_ = file.split("/")[-1]
+        username, sub_timestamp, task = file_.replace(
+            ".json", "").split("-_-_-")
+        json_data = json.load(open(file))
+        public_score = json.dumps(json_data.get("public_scores", {}))
+        submissions.append({"user": username, "task": task, "public_score": public_score,
+                           "submitted_time": sub_timestamp})
+
+    last_submission_update_time = datetime.now(timezone.utc)
+    return hub_path
+
+
+hub_path = refresh_submissions()
+hub_dir = hub_path.split("snapshot")[0] + "snapshot"
+
+
+def time_ago(submitted_time):
+    return str(datetime.fromtimestamp(int(submitted_time), tz=timezone.utc)) + " UTC"
+
+
+def filter_submissions(task_type, search_query):
+    if search_query == "":
+        filtered = [s for s in submissions if task_type ==
+                    "all" or s["task"] == task_type]
+    else:
+        filtered = [s for s in submissions if (
+            task_type == "all" or s["task"] == task_type) and search_query.lower() in s["user"].lower()]
+    return [{"user": s["user"], "task": s["task"], "public_score": s["public_score"], "submitted_time": time_ago(s["submitted_time"])} for s in filtered]
+
+
+def display_submissions(task_type="all", search_query=""):
+    if submissions is None or ((datetime.now(timezone.utc) - last_submission_update_time).total_seconds() > 3600):
+        refresh_submissions()
+    filtered_submissions = filter_submissions(task_type, search_query)
+    return [[s["user"], s["task"], s["submitted_time"], s["public_score"]] for s in filtered_submissions]
+
+
+def add_submission(file):
+    global submissions
+    try:
+        with open(file, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+
+        filename = os.path.basename(file)
+        username, sub_timestamp, task = filename.replace(
+            ".json", "").split("-_-_-")
+        submission_time = datetime.fromtimestamp(
+            int(sub_timestamp), tz=timezone.utc)
+
+        assert task in ["task1", "task2"], "Invalid task type"
+        assert len(username) > 0, "Invalid username"
+        assert submission_time < datetime.now(
+            timezone.utc), "Invalid submission time"
+
+        upload_file(
+            repo_type="dataset",
+            path_or_fileobj=file,
+            path_in_repo=task + "/" + filename,
+            repo_id=SUBMISSION_REPO
+        )
+        refresh_submissions()
+        return "💪🏆🎉 Submissions registered successfully to the system!"
+    except Exception as e:
+        return f"❌ Error adding submission: {e}"
+
+
+def refresh_page():
+    return "Pong! Submission server is alive! 😊"
+
+
+# Define Gradio Interface
+with gr.Blocks(title="🌟 MediaEval Medico 2025 Submissions 🌟") as demo:
+    gr.Markdown("""
+# 🌟 Welcome to the official submission portal for the **[MediaEval Medico 2025](https://multimediaeval.github.io/editions/2025/tasks/medico/)** challenge! 🏥🔍  
+### 📋 [**GitHub Repository**](https://github.com/simula/MediaEval-Medico-2025) | 🔗 [**MediaEval 2025 Task Page**](https://multimediaeval.github.io/editions/2025/tasks/medico/)  
+---
+The **MediaEval Medico 2025 Challenge** 🔬 focuses on **Visual Question Answering (VQA)** for **Gastrointestinal (GI) imaging**, emphasizing **explainability** 🤔📖 to foster **trustworthy AI** for clinical adoption ⚕️.
+This task continues the long-running **Medico series** at MediaEval and leverages the newly developed **Kvasir-VQA-x1** dataset, enabling **multimodal reasoning** and **interpretable clinical decision support** 📈.
+### 📦 [**Available Datasets**](https://github.com/simula/MediaEval-Medico-2025#-dataset-overview-kvasir-vqa-x1) | 🧠 [**Task Details & Training Resources**](https://github.com/simula/MediaEval-Medico-2025?tab=readme-ov-file#-task-descriptions) | 📝 [**Submission Instructions**](https://github.com/simula/MediaEval-Medico-2025#submission)  
+---
+""")
+
+    with gr.Tab("View Submissions"):
+        gr.Markdown("### Filter and Search Submissions")
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                task_type_dropdown = gr.Dropdown(
+                    choices=["all", "task1", "task2"],
+                    value="all",
+                    label="Task Type"
+                )
+                search_box = gr.Textbox(
+                    label="Search by Username",
+                    placeholder="Enter username..."
+                )
+
+            with gr.Column(scale=6):
+                output_table = gr.Dataframe(
+                    headers=["User", "Task", "Submitted Time", "Public Score"],
+                    interactive=False,
+                    wrap=True,
+                    column_widths=["100px", "50px", "80px", "200px"],
+                    label="Submissions"
+                )
+
+        task_type_dropdown.change(
+            fn=display_submissions,
+            inputs=[task_type_dropdown, search_box],
+            outputs=output_table
+        )
+        search_box.change(
+            fn=display_submissions,
+            inputs=[task_type_dropdown, search_box],
+            outputs=output_table
+        )
+
+        gr.Markdown(
+            f'''
+            🔄 Last refreshed: {last_submission_update_time.strftime('%Y-%m-%d %H:%M:%S')} UTC |  📊 Total Submissions: {len(submissions)}
+
+            💬 For any questions or issues, [contact the organizers](https://github.com/simula/MediaEval-Medico-2025#-organizers) or check the documentation in the [GitHub repo](https://github.com/simula/MediaEval-Medico-2025).  Good luck and thank you for contributing to medical AI research! 💪🤖🌍
+            ''')
+
+    with gr.Tab("Upload Submission", visible=False):
+        file_input = gr.File(label="Upload JSON", file_types=[".json"])
+        upload_output = gr.Textbox(label="Upload Result")
+        file_input.upload(fn=add_submission,
+                          inputs=file_input, outputs=upload_output)
+
+    with gr.Tab("Refresh API", visible=False):
+        refresh_button = gr.Button("Refresh")
+        status_output = gr.Textbox(label="Status")
+        refresh_button.click(fn=refresh_page, inputs=[], outputs=status_output)
+
+    demo.load(lambda: display_submissions("all", ""),
+              inputs=[], outputs=output_table)
+
+demo.launch()

medvqa/competitions/medico-2025/task_1.py

@@ -0,0 +1,162 @@
+from gradio_client import Client, handle_file
+from huggingface_hub import snapshot_download, login, whoami
+import argparse
+import os
+import subprocess as sp
+import time
+from datetime import datetime, timezone
+import shutil  # Add this import
+import json
+from huggingface_hub import HfApi, grant_access
+import re
+
+HF_GATE_ACESSLIST = ["SushantGautam",
+                     "stevenah", "vlbthambawita"]
+
+MEDVQA_SUBMIT = True if os.environ.get(
+    '_MEDVQA_SUBMIT_FLAG_', 'FALSE') == 'TRUE' else False
+parser = argparse.ArgumentParser(description='Run GI-1015 Task 1 (VQA)')
+parser.add_argument('--repo_id', type=str, required=True,
+                    help='Path to the HF submission repository')
+args, _ = parser.parse_known_args()
+
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+submission_file = "submission_task1.py"
+file_from_validation = "predictions_1.json"
+
+min_library = ["datasets>=3.4.1", "transformers", "evaluate",
+               "rouge_score", 'tqdm', "gradio_client>=1.8.0"]
+
+print("🌟 ImageCLEFmed-MEDVQA-GI-2025 🌟",
+      "https://github.com/simula/ImageCLEFmed-MEDVQA-GI-2025")
+print("🔍 Subtask 1: Algorithm Development for Question Interpretation and Response")
+print(f"👀 Analyzing submission repository: {args.repo_id} 👀")
+
+try:
+    print(f"Logged in to HuggingFace as: {whoami()['name']}")
+except Exception:
+    print("⚠️⚠️ Not logged in to HuggingFace! Please get your login token from https://huggingface.co/settings/tokens 🌐")
+    login()
+
+client = Client("SimulaMet/medvqa")
+print("💓 Communicating with the Submission Server: Ping!")
+result = client.predict(
+    api_name="/refresh_page"
+)
+print(result)
+
+
+hf_username = whoami()['name']
+assert len(hf_username) > 0, "🚫 HuggingFace login failed for some reason"
+current_timestamp = int(time.time())
+
+snap_dir = snapshot_download(
+    repo_id=args.repo_id, allow_patterns=[submission_file, "requirements.txt"])
+
+if not os.path.isfile(os.path.join(snap_dir, submission_file)):
+    raise FileNotFoundError(
+        f"Submission file '{submission_file}' not found in the repository!")
+
+if os.path.isfile(os.path.join(snap_dir, file_from_validation)):
+    os.remove(os.path.join(snap_dir, file_from_validation))
+
+print("📦 Making sure of the minimum requirements to run the script 📦")
+sp.run(["python", "-m", "pip", "install", "-q"] + min_library, check=True)
+
+if os.path.isfile(os.path.join(snap_dir, "requirements.txt")):
+    print(
+        f"📦 Installing requirements from the submission repo: {args.repo_id}/requirements.txt")
+    sp.run(["python", "-m", "pip", "install", "-q", "-r",
+            f"{snap_dir}/requirements.txt"], cwd=snap_dir, check=True)
+
+
+if os.environ.get("_MEDVQA_CHALLENGE_EVALUATE_FLAG_", "FALSE") == "TRUE":
+    # Patch submission file for challenge evaluation
+    challenge_file = submission_file.replace(".py", "_challenge.py")
+    submission_path = os.path.join(snap_dir, submission_file)
+    challenge_path = os.path.join(snap_dir, challenge_file)
+    with open(submission_path, "r", encoding="utf-8") as f:
+        code = f.read()
+    # Use regex to match the line, ignoring whitespace
+    pattern = r'val_dataset\s*=\s*load_dataset\(\s*["\']SimulaMet/Kvasir-VQA-test["\']\s*,\s*split\s*=\s*["\']validation["\']\s*\)'
+    new_line = 'val_dataset = load_dataset("SimulaMet/Kvasir-VQA-private", split="test")'
+    if re.search(pattern, code):
+        code = re.sub(pattern, new_line, code)
+        with open(challenge_path, "w", encoding="utf-8") as f:
+            f.write(code)
+        submission_file = challenge_file
+        print(f"🔄 Challenge file created at: {challenge_path}")
+    else:
+        print("⚠️ Challenge patch not applied: expected line not found in submission file.")
+        os.exit(
+            "Please check the submission file for compatibility with challenge evaluation.")
+
+
+sp.run(["python", f"{snap_dir}/{submission_file}"],
+       cwd=snap_dir, check=True)
+print(
+    f"🎉 The submission script ran successfully, the intermediate files are at {snap_dir}")
+
+if not MEDVQA_SUBMIT:
+    print("\n You can now run medvqa validate_and_submit .... command to submit the task.")
+else:
+    print("🚀 Preparing for submission 🚀")
+    file_path_to_upload = os.path.join(
+        snap_dir, f"{hf_username}-_-_-{current_timestamp}-_-_-task1.json")
+    shutil.copy(os.path.join(snap_dir, file_from_validation),
+                file_path_to_upload)  # Use shutil.copy here
+    # add repo_id to the submission file
+    with open(file_path_to_upload, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+        data['repo_id'] = args.repo_id
+        with open(file_path_to_upload, 'w', encoding='utf-8') as f:
+            json.dump(data, f, ensure_ascii=False)
+    api = HfApi()
+    api.update_repo_visibility(args.repo_id, private=False)  # Make public
+    api.update_repo_settings(
+        args.repo_id, gated='manual')  # Enable gated access
+    for user in HF_GATE_ACESSLIST:
+        try:
+            grant_access(args.repo_id, user)  # Grant access
+        except Exception as e:
+            print(user, ":", e)
+    print(
+        f'''✅ {args.repo_id} model is now made public, but gated, and is shared with organizers.
+        You should not make the model private or remove/update it until the competition results are announced.
+        Feel feel to re-submit the task if you change the model on the repository.
+        We will notify you if there are any issues with the submission.
+        ''')
+
+    result = client.predict(
+        file=handle_file(file_path_to_upload),
+        api_name="/add_submission"
+    )
+    print({"User": hf_username, "Task": "task1",
+           "Submitted_time": str(datetime.fromtimestamp(int(current_timestamp), tz=timezone.utc)) + " UTC"
+           })
+    print(result)
+    print("Visit this URL to see the entry: 👇")
+    Client("SimulaMet/medvqa")
+
+
+if os.environ.get("_MEDVQA_CHALLENGE_EVALUATE_FLAG_", "FALSE") == "TRUE":
+    src_json = os.path.join(snap_dir, "predictions_1.json")
+    if os.path.isfile(src_json):
+        with open(src_json, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        # Remove 'debug' key if present
+        data.pop("debug", None)
+        # Rename 'public_scores' to 'challenge_scores' if present
+        if "public_scores" in data:
+            data["challenge_scores"] = data.pop("public_scores")
+        # Get Team_Name from submission_info
+        team_name = data.get("submission_info", {}).get(
+            "Team_Name", "unknown_team")
+        team_name_safe = re.sub(r'[^a-zA-Z0-9_\-]', '_', team_name)
+        out_json = os.path.join(os.getcwd(), f"task1_{team_name_safe}.json")
+        with open(out_json, "w", encoding="utf-8") as f:
+            json.dump(data, f, ensure_ascii=False, indent=2)
+        print(f"✅ Copied and processed predictions to: {out_json}")
+    else:
+        print("❌ predictions_1.json not found in snapshot directory!")
+    # === End: Post-processing predictions_1.json ===

medvqa/competitions/medico-2025/task_2.py

@@ -0,0 +1,186 @@
+from gradio_client import Client, handle_file
+from huggingface_hub import snapshot_download, login, whoami
+import argparse
+import os
+import subprocess as sp
+import time
+from datetime import datetime, timezone
+import shutil  # Add this import
+import json
+from huggingface_hub import HfApi, grant_access
+import re
+
+HF_GATE_ACESSLIST = ["SushantGautam",
+                     "stevenah", "vlbthambawita"]
+
+MEDVQA_SUBMIT = True if os.environ.get(
+    '_MEDVQA_SUBMIT_FLAG_', 'FALSE') == 'TRUE' else False
+parser = argparse.ArgumentParser(
+    description='Run GI-1015 Task 2 (Image Generation)')
+parser.add_argument('--repo_id', type=str, required=True,
+                    help='Path to the HF submission repository')
+args, _ = parser.parse_known_args()
+
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+submission_file = "submission_task2.py"
+file_from_validation = "predictions_2.json"
+
+min_library = ["datasets>=3.4.1", "transformers", "evaluate", "scipy", "scikit-learn", "diffusers", "peft",
+               "rouge_score", 'tqdm', "gradio_client>=1.8.0"]
+
+print("🌟 ImageCLEFmed-MEDVQA-GI-2025 🌟",
+      "https://github.com/simula/ImageCLEFmed-MEDVQA-GI-2025")
+print("🔍 Subtask 2: Creation of High-Fidelity Synthetic GI Images")
+print(f"👀 Analyzing submission repository: {args.repo_id} 👀")
+
+try:
+    print(f"Logged in to HuggingFace as: {whoami()['name']}")
+except Exception:
+    print("⚠️⚠️ Not logged in to HuggingFace! Please get your login token from https://huggingface.co/settings/tokens 🌐")
+    login()
+
+client = Client("SimulaMet/medvqa")
+print("💓 Communicating with the Submission Server: Ping!")
+result = client.predict(
+    api_name="/refresh_page"
+)
+print(result)
+
+
+hf_username = whoami()['name']
+assert len(hf_username) > 0, "🚫 HuggingFace login failed for some reason"
+current_timestamp = int(time.time())
+
+snap_dir = snapshot_download(
+    repo_id=args.repo_id, allow_patterns=[submission_file, "requirements.txt"])
+
+if not os.path.isfile(os.path.join(snap_dir, submission_file)):
+    raise FileNotFoundError(
+        f"Submission file '{submission_file}' not found in the repository!")
+
+if os.path.isfile(os.path.join(snap_dir, file_from_validation)):
+    os.remove(os.path.join(snap_dir, file_from_validation))
+
+print("📦 Making sure of the minimum requirements to run the script 📦")
+sp.run(["python", "-m", "pip", "install", "-q"] + min_library, check=True)
+
+if os.path.isfile(os.path.join(snap_dir, "requirements.txt")):
+    print(
+        f"📦 Installing requirements from the submission repo: {args.repo_id}/requirements.txt")
+    sp.run(["python", "-m", "pip", "install", "-q", "-r",
+            f"{snap_dir}/requirements.txt"], cwd=snap_dir, check=True)
+
+if os.environ.get("_MEDVQA_CHALLENGE_EVALUATE_FLAG_", "FALSE") == "TRUE":
+    # Patch submission file for challenge evaluation
+    challenge_file = submission_file.replace(".py", "_challenge.py")
+    submission_path = os.path.join(snap_dir, submission_file)
+    challenge_path = os.path.join(snap_dir, challenge_file)
+    with open(submission_path, "r", encoding="utf-8") as f:
+        code = f.read()
+    # Replace only the dataset string
+    if "SimulaMet/Kvasir-VQA-test" in code:
+        code = code.replace("SimulaMet/Kvasir-VQA-test",
+                            "SimulaMet/Kvasir-VQA-private")
+        code = code.replace('"validation"', '"test"')
+        # Comment out specific lines
+        lines = code.splitlines()
+        for i, line in enumerate(lines):
+            if ("huggingface.co/datasets" in line or
+                re.search(r'^\s*prompt_to_real\s*=', line) or
+                    re.search(r'^\s*jsons__\s*=', line)):
+                if not line.lstrip().startswith("#"):
+                    leading_ws = len(line) - len(line.lstrip())
+                    lines[i] = line[:leading_ws] + "# " + line[leading_ws:]
+        # Insert new code block after 'import requests'
+        for i, line in enumerate(lines):
+            if "import requests" in line:
+                insert_idx = i + 1
+                break
+        else:
+            insert_idx = None
+        new_block = [
+            'from huggingface_hub import hf_hub_download',
+            'prompt_to_real = json.load(open(hf_hub_download("SimulaMet/Kvasir-VQA-private", "real_mapping", repo_type="dataset")))',
+            'jsons__ = json.load(open(hf_hub_download("SimulaMet/Kvasir-VQA-private", "imagen-test", repo_type="dataset")))',
+        ]
+        if insert_idx is not None:
+            lines[insert_idx:insert_idx] = new_block
+        code = "\n".join(lines)
+        with open(challenge_path, "w", encoding="utf-8") as f:
+            f.write(code)
+        submission_file = challenge_file
+        print(f"🔄 Challenge file created at: {challenge_path}")
+    else:
+        print(
+            "⚠️ Challenge patch not applied: expected string not found in submission file.")
+        os.exit(
+            "Please check the submission file for compatibility with challenge evaluation.")
+
+print("🔍 Starting your script and loading submission details...")
+sp.run(["python", f"{snap_dir}/{submission_file}"],
+       cwd=snap_dir, check=True)
+print(
+    f"🎉 The submission script ran successfully, the intermediate files are at {snap_dir}")
+
+if not MEDVQA_SUBMIT:
+    print("\n You can now run medvqa validate_and_submit .... command to submit the task.")
+else:
+    print("🚀 Preparing for submission 🚀")
+    file_path_to_upload = os.path.join(
+        snap_dir, f"{hf_username}-_-_-{current_timestamp}-_-_-task2.json")
+    shutil.copy(os.path.join(snap_dir, file_from_validation),
+                file_path_to_upload)  # Use shutil.copy here
+    # add repo_id to the submission file
+    with open(file_path_to_upload, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+        data['repo_id'] = args.repo_id
+        with open(file_path_to_upload, 'w', encoding='utf-8') as f:
+            json.dump(data, f, ensure_ascii=False)
+    api = HfApi()
+    api.update_repo_visibility(args.repo_id, private=False)  # Make public
+    api.update_repo_settings(
+        args.repo_id, gated='manual')  # Enable gated access
+    for user in HF_GATE_ACESSLIST:
+        try:
+            grant_access(args.repo_id, user)  # Grant access
+        except Exception as e:
+            print(user, ":", e)
+    print(
+        f'''✅ {args.repo_id} model is now made public, but gated, and is shared with organizers.
+        You should not make the model private or remove/update it until the competition results are announced.
+        Feel feel to re-submit the task if you change the model on the repository.
+        We will notify you if there are any issues with the submission.
+        ''')
+
+    result = client.predict(
+        file=handle_file(file_path_to_upload),
+        api_name="/add_submission"
+    )
+    print({"User": hf_username, "Task": "task2",
+           "Submitted_time": str(datetime.fromtimestamp(int(current_timestamp), tz=timezone.utc)) + " UTC"
+           })
+    print(result)
+    print("Visit this URL to see the entry: 👇")
+    Client("SimulaMet/medvqa")
+
+if os.environ.get("_MEDVQA_CHALLENGE_EVALUATE_FLAG_", "FALSE") == "TRUE":
+    src_json = os.path.join(snap_dir, "predictions_2.json")
+    if os.path.isfile(src_json):
+        with open(src_json, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        # Remove 'debug' key if present
+        data.pop("debug", None)
+        # Rename 'public_scores' to 'challenge_scores' if present
+        if "public_scores" in data:
+            data["challenge_scores"] = data.pop("public_scores")
+        # Get Team_Name from submission_info
+        team_name = data.get("submission_info", {}).get(
+            "Team_Name", "unknown_team")
+        team_name_safe = re.sub(r'[^a-zA-Z0-9_\-]', '_', team_name)
+        out_json = os.path.join(os.getcwd(), f"task2_{team_name_safe}.json")
+        with open(out_json, "w", encoding="utf-8") as f:
+            json.dump(data, f, ensure_ascii=False, indent=2)
+        print(f"✅ Copied and processed predictions to: {out_json}")
+    else:
+        print("❌ predictions_1.json not found in snapshot directory!")
+    # === End: Post-processing predictions_1.json ===

medvqa/submission_samples/medico-2025/submission_task1.py

@@ -0,0 +1,148 @@
+from transformers import AutoModelForCausalLM
+from datasets import load_dataset
+from transformers import AutoProcessor
+import torch
+import json
+import time
+from tqdm import tqdm
+import subprocess
+import platform
+import sys
+
+from evaluate import load
+
+bleu = load("bleu")
+rouge = load("rouge")
+meteor = load("meteor")
+
+
+val_dataset = load_dataset("SimulaMet/Kvasir-VQA-test", split="validation")
+predictions = []  # List to store predictions
+
+gpu_name = torch.cuda.get_device_name(
+    0) if torch.cuda.is_available() else "cpu"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def get_mem(): return torch.cuda.memory_allocated(device) / \
+    (1024 ** 2) if torch.cuda.is_available() else 0
+
+
+initial_mem = get_mem()
+
+# ✏️✏️--------EDIT SECTION 1: SUBMISISON DETAILS and MODEL LOADING --------✏️✏️#
+
+SUBMISSION_INFO = {
+    # 🔹 TODO: PARTICIPANTS MUST ADD PROPER SUBMISSION INFO FOR THE SUBMISSION 🔹
+    # This will be visible to the organizers
+    # DONT change the keys, only add your info
+    "Participant_Names": "Sushant Gautam, Steven Hicks and Vajita Thambawita",
+    "Affiliations": "SimulaMet",
+    "Contact_emails": ["[email protected]", "[email protected]"],
+    # But, the first email only will be used for correspondance
+    "Team_Name": "SimulaMetmedVQA Rangers",
+    "Country": "Norway",
+    "Notes_to_organizers": '''
+        eg, We have finetund XXX model
+        This is optional . .
+        Used data augmentations . .
+        Custom info about the model . .
+        Any insights. .
+        + Any informal things you like to share about this submission.
+        '''
+}
+# 🔹 TODO: PARTICIPANTS MUST LOAD THEIR MODEL HERE, EDIT AS NECESSARY FOR YOUR MODEL 🔹
+# can add necessary library imports here
+
+model_hf = AutoModelForCausalLM.from_pretrained(
+    "SushantGautam/Florence-2-vqa-demo", trust_remote_code=True).to(device)
+processor = AutoProcessor.from_pretrained(
+    "microsoft/Florence-2-base-ft", trust_remote_code=True)
+
+model_hf.eval()  # Ensure model is in evaluation mode
+# 🏁----------------END  SUBMISISON DETAILS and MODEL LOADING -----------------🏁#
+
+start_time, post_model_mem = time.time(), get_mem()
+total_time, final_mem = round(
+    time.time() - start_time, 4), round(get_mem() - post_model_mem, 2)
+model_mem_used = round(post_model_mem - initial_mem, 2)
+
+for idx, ex in enumerate(tqdm(val_dataset, desc="Validating")):
+    question = ex["question"]
+    image = ex["image"].convert(
+        "RGB") if ex["image"].mode != "RGB" else ex["image"]
+    # you have access to 'question' and 'image' variables for each example
+
+# ✏️✏️___________EDIT SECTION 2: ANSWER GENERATION___________✏️✏️#
+    # 🔹 TODO: PARTICIPANTS CAN MODIFY THIS TOKENIZATION STEP IF NEEDED 🔹
+    inputs = processor(text=[question], images=[image],
+                       return_tensors="pt", padding=True)
+    inputs = {k: v.to(device) for k, v in inputs.items()
+              if k not in ['labels', 'attention_mask']}
+
+    # 🔹 TODO: PARTICIPANTS CAN MODIFY THE GENERATION AND DECODING METHOD HERE 🔹
+    with torch.no_grad():
+        output = model_hf.generate(**inputs)
+    answer = processor.tokenizer.decode(output[0], skip_special_tokens=True)
+    # make sure 'answer' variable will hold answer (sentence/word) as str
+# 🏁________________ END ANSWER GENERATION ________________🏁#
+
+# ⛔ DO NOT EDIT any lines below from here, can edit only upto decoding step above as required. ⛔
+    # Ensures answer is a string
+    assert isinstance(
+        answer, str), f"Generated answer at index {idx} is not a string"
+    # Appends prediction
+    predictions.append(
+        {"index": idx, "img_id": ex["img_id"], "question": ex["question"], "answer": answer})
+
+# Ensure all predictions match dataset length
+assert len(predictions) == len(
+    val_dataset), "Mismatch between predictions and dataset length"
+
+total_time, final_mem = round(
+    time.time() - start_time, 4), round(get_mem() - post_model_mem, 2)
+model_mem_used = round(post_model_mem - initial_mem, 2)
+
+# caulcualtes metrics
+references = [[e] for e in val_dataset['answer']]
+preds = [pred['answer'] for pred in predictions]
+
+bleu_result = bleu.compute(predictions=preds, references=references)
+rouge_result = rouge.compute(predictions=preds, references=references)
+meteor_result = meteor.compute(predictions=preds, references=references)
+bleu_score = round(bleu_result['bleu'], 2)
+rouge1_score = round(float(rouge_result['rouge1']), 2)
+rouge2_score = round(float(rouge_result['rouge2']), 2)
+rougeL_score = round(float(rouge_result['rougeL']), 2)
+meteor_score = round(float(meteor_result['meteor']), 2)
+
+public_scores = {
+    'bleu': bleu_score,
+    'rouge1': rouge1_score,
+    'rouge2': rouge2_score,
+    'rougeL': rougeL_score,
+    'meteor': meteor_score
+}
+print("✨Public scores: ", public_scores)
+
+# Saves predictions to a JSON file
+
+output_data = {"submission_info": SUBMISSION_INFO, "public_scores": public_scores,
+               "predictions": predictions, "total_time": total_time, "time_per_item": total_time / len(val_dataset),
+               "memory_used_mb": final_mem, "model_memory_mb": model_mem_used, "gpu_name": gpu_name,
+               "debug": {
+                   "packages": json.loads(subprocess.check_output([sys.executable, "-m", "pip", "list", "--format=json"])),
+                   "system": {
+                       "python": platform.python_version(),
+                       "os": platform.system(),
+                       "platform": platform.platform(),
+                       "arch": platform.machine()
+                   }}}
+
+
+with open("predictions_1.json", "w") as f:
+    json.dump(output_data, f, indent=4)
+print(f"Time: {total_time}s | Mem: {final_mem}MB | Model Load Mem: {model_mem_used}MB | GPU: {gpu_name}")
+print("✅ Scripts Looks Good! Generation process completed successfully. Results saved to 'predictions_1.json'.")
+print("Next Step:\n 1) Upload this submission_task1.py script file to HuggingFace model repository.")
+print('''\n 2) Make a submission to the competition:\n Run:: medvqa validate_and_submit --competition=medico-2025 --task=1 --repo_id=...''')