Created using Colab

notebooks/01_model_training_and_fine_tuning.ipynb

@@ -5,7 +5,7 @@
     "colab": {
       "provenance": [],
       "gpuType": "T4",
-      "authorship_tag": "ABX9TyPSiHqZC3XJwRUnsyWT6QPo",
+      "authorship_tag": "ABX9TyM7c25lrmEww8m7ZUcLm9mv",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -51,12 +51,12 @@
       "metadata": {
         "id": "OwBert5NpQwF"
       },
-      "execution_count": 5,
+      "execution_count": null,
       "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -143,7 +143,7 @@
         "id": "ikceEotXOU_1",
         "outputId": "077395cc-6cf0-42f5-e0b3-65a1e322b5a8"
       },
-      "execution_count": 2,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -199,7 +199,7 @@
         },
         "outputId": "3a3b35dc-01e5-4980-bdfc-0958467a5f24"
       },
-      "execution_count": 3,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -274,7 +274,7 @@
         "id": "3cTqFqcvnVG2",
         "outputId": "19533754-f990-4485-b848-d9c2be3f782b"
       },
-      "execution_count": 7,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -405,7 +405,7 @@
         "id": "HrV6nUsjodCM",
         "outputId": "ff77d837-8183-4f16-ea06-4979fd6e540a"
       },
-      "execution_count": 8,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -523,7 +523,7 @@
           "height": 773
         }
       },
-      "execution_count": 10,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -591,6 +591,163 @@
         "id": "DWEkHU0AzaZJ"
       }
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **Hugging Face**"
+      ],
+      "metadata": {
+        "id": "fjado_xqZDQJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 1. Retrieve the token (Checks Colab Secrets first, then Environment variables)\n",
+        "try:\n",
+        "    hf_token = userdata.get('HF_TOKEN')\n",
+        "except userdata.SecretNotFoundError:\n",
+        "    hf_token = os.environ.get(\"HF_TOKEN\")\n",
+        "\n",
+        "if not hf_token:\n",
+        "    raise ValueError(\"HF_TOKEN not found! Please set it in Colab Secrets or export it.\")\n",
+        "\n",
+        "# 2. Define your repository setup\n",
+        "hf_username = \"<YOUR_HF_USERNAME>\" # e.g., mobadara\n",
+        "repo_name = \"pneumonia-densenet121\"\n",
+        "repo_id = f\"{hf_username}/{repo_name}\"\n",
+        "model_path = \"densenet_pneumonia_finetuned.pth\"\n",
+        "\n",
+        "# 3. Create the repository\n",
+        "api = HfApi(token=hf_token)\n",
+        "print(f\"Creating/Checking repository: {repo_id}...\")\n",
+        "create_repo(repo_id=repo_id, token=hf_token, exist_ok=True)\n",
+        "\n",
+        "# 4. Generate the Model Card with YAML Metadata\n",
+        "model_card_content = \"\"\"---\n",
+        "language: en\n",
+        "license: mit\n",
+        "tags:\n",
+        "- image-classification\n",
+        "- vision\n",
+        "- pytorch\n",
+        "- densenet\n",
+        "- medical\n",
+        "- healthcare\n",
+        "datasets:\n",
+        "- paultimothymooney/chest-xray-pneumonia\n",
+        "metrics:\n",
+        "- accuracy\n",
+        "- recall\n",
+        "---\n",
+        "# DenseNet121 - Pediatric Pneumonia Detection\n",
+        "\n",
+        "## Model Description\n",
+        "This is a fine-tuned DenseNet121 model trained to classify chest X-rays into two categories: **NORMAL** and **PNEUMONIA**. It was developed as part of a precision diagnostics AI pipeline to assist clinical workflows.\n",
+        "\n",
+        "To handle the severe class imbalance inherent in the dataset, dynamic class weights were applied to the PyTorch `CrossEntropyLoss` function, heavily penalizing false negatives to prioritize patient safety.\n",
+        "\n",
+        "## Intended Uses & Limitations\n",
+        "* **Intended Use:** As an auxiliary tool for clinical triage and radiologist workflow acceleration. It is designed to be paired with explainability tools (like Grad-CAM) to provide visual context.\n",
+        "* **Limitations:** This model is a research prototype and is not FDA-approved. It should not be used as a standalone diagnostic tool.\n",
+        "\n",
+        "## Training Data\n",
+        "Trained on the Kaggle Chest X-Ray Images (Pneumonia) dataset. All images were normalized and resized to 224x224. Light data augmentation (rotation and color jittering) was applied to the training set to prevent overfitting.\n",
+        "\n",
+        "## Evaluation Results\n",
+        "The model was evaluated on a completely unseen test dataset consisting of 624 images.\n",
+        "* **Overall Accuracy:** 86.4%\n",
+        "* **Pneumonia Recall (Sensitivity):** 94.6% (Successfully identified 369 out of 390 sick patients)\n",
+        "\n",
+        "The high recall indicates the model heavily prioritizes catching pathology, treating false positives as an acceptable clinical trade-off requiring secondary physician review.\n",
+        "\"\"\"\n",
+        "\n",
+        "# Save the card locally\n",
+        "with open(\"README.md\", \"w\") as f:\n",
+        "    f.write(model_card_content)\n",
+        "\n",
+        "# 5. Upload the files\n",
+        "print(\"Uploading Model Weights...\")\n",
+        "api.upload_file(\n",
+        "    path_or_fileobj=model_path,\n",
+        "    path_in_repo=model_path,\n",
+        "    repo_id=repo_id,\n",
+        "    commit_message=\"Initial push: Fine-tuned DenseNet weights\"\n",
+        ")\n",
+        "\n",
+        "print(\"Uploading Model Card...\")\n",
+        "api.upload_file(\n",
+        "    path_or_fileobj=\"README.md\",\n",
+        "    path_in_repo=\"README.md\",\n",
+        "    repo_id=repo_id,\n",
+        "    commit_message=\"Add professional model card with YAML metadata\"\n",
+        ")\n",
+        "\n",
+        "print(f\"✅ Successfully deployed to Hugging Face! View it here: https://huggingface.co/{repo_id}\")"
+      ],
+      "metadata": {
+        "id": "Dwm-2sodZCUk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **GitHub Release**"
+      ],
+      "metadata": {
+        "id": "Javrpd_gZZiK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Safely extract the token and assign it to an environment variable\n",
+        "os.environ[\"GITHUB_TOKEN\"] = userdata.get(\"GITHUB_TOKEN\")\n",
+        "print(\"✅ Token securely loaded into environment!\")"
+      ],
+      "metadata": {
+        "id": "Uq2QnjXJZr_g"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%%bash\n",
+        "%%bash\n",
+        "# 1. Variables (Notice GITHUB_TOKEN is missing here, we read it directly from the environment!)\n",
+        "REPO_OWNER=\"mobadara\"\n",
+        "REPO_NAME=\"precision-diagnostics-xai\"\n",
+        "MODEL_FILE=\"densenet_pneumonia_finetuned.pth\"\n",
+        "TAG=\"v1.0.0\"\n",
+        "\n",
+        "# 2. Install the GitHub CLI into Colab\n",
+        "curl -fsSL https://cli.github.com/packages/githubcli-archive-keyring.gpg | sudo dd of=/usr/share/keyrings/githubcli-archive-keyring.gpg \\\n",
+        "&& sudo chmod go+r /usr/share/keyrings/githubcli-archive-keyring.gpg \\\n",
+        "&& echo \"deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main\" | sudo tee /etc/apt/sources.list.d/github-cli.list > /dev/null \\\n",
+        "&& sudo apt update -q && sudo apt install gh -y -q\n",
+        "\n",
+        "# 3. Authenticate GitHub CLI securely using the environment variable\n",
+        "echo \"$GITHUB_TOKEN\" | gh auth login --with-token\n",
+        "\n",
+        "# 4. Create the Release and upload the file simultaneously\n",
+        "echo \"Creating GitHub Release $TAG...\"\n",
+        "gh release create $TAG $MODEL_FILE \\\n",
+        "  --repo \"$REPO_OWNER/$REPO_NAME\" \\\n",
+        "  --title \"v1.0.0 - Baseline DenseNet121 Model\" \\\n",
+        "  --notes \"Initial release of the fine-tuned DenseNet121 weights for Pneumonia detection. Achieved 94.6% recall on the test set. File attached for backend deployment.\"\n",
+        "\n",
+        "echo \"✅ Successfully created GitHub Release and uploaded weights!\""
+      ],
+      "metadata": {
+        "id": "o4_40DdsZhIb"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "source": [