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README.md

@@ -1,14 +1,14 @@
----
-title: Clinical Decision Aid Mammogram
-emoji: 💻
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 5.7.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Diagnosis of breast type in breast cancer from mammography
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Clinical Decision Aid Mammogram
+emoji: 💻
+colorFrom: purple
+colorTo: green
+sdk: gradio
+sdk_version: 5.7.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Diagnosis of breast type in breast cancer from mammography
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import pdfkit
+# path_wkhtmltopdf = "/usr/bin/wkhtmltopdf"
+# config = pdfkit.configuration(wkhtmltopdf=path_wkhtmltopdf)
+import subprocess
+
+try:
+    path_wkhtmltopdf = subprocess.check_output(['which', 'wkhtmltopdf']).decode('utf-8').strip()
+    config = pdfkit.configuration(wkhtmltopdf=path_wkhtmltopdf)
+except subprocess.CalledProcessError:
+    raise FileNotFoundError("wkhtmltopdf not found. Ensure it is installed in your environment.")
+
+
+# import tensorflow as tf
+import numpy as np
+from PIL import Image
+import cv2
+import gradio as gr
+# from numpy import asarray
+from transformers import pipeline
+
+from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, BatchNormalization, Dropout,AveragePooling2D
+import tensorflow as tf
+from tensorflow.keras.applications import DenseNet201
+from keras.models import Model
+from keras.models import Sequential
+from keras.regularizers import *
+from tensorflow import keras
+from tensorflow.keras import layers
+
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from PIL import Image
+import cv2
+from transformers import pipeline
+import os
+
+# تابع پیش‌بینی
+def predict_demo(image, model_name):
+    if model_name == "how dense is":
+        image = np.asarray(image)
+
+        # مدل اول
+        def load_model():
+            model = tf.keras.models.load_model("model.h5", compile=False)
+            model.compile(optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.00001, decay=0.0001),
+                          metrics=["accuracy"], loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1))
+            model.load_weights("modeldense1.h5")
+            return model
+
+        model = load_model()
+
+        def preprocess(image):
+            image = cv2.resize(image, (224, 224))
+            kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
+            im = cv2.filter2D(image, -1, kernel)
+            if im.ndim == 3:
+                # اضافه کردن بعد جدید برای ورودی مدل
+                im = np.expand_dims(im, axis=0)
+            elif im.ndim == 2:
+                # اگر تصویر سیاه و سفید باشد
+                im = np.expand_dims(im, axis=-1)
+                im = np.repeat(im, 3, axis=-1)
+                im = np.expand_dims(im, axis=0)
+            return im
+
+
+        class_name = ['Benign with Density=1', 'Malignant with Density=1', 'Benign with Density=2',
+                      'Malignant with Density=2', 'Benign with Density=3', 'Malignant with Density=3',
+                      'Benign with Density=4', 'Malignant with Density=4']
+
+        def predict_img(img):
+            img = preprocess(img)
+            img = img / 255.0
+            pred = model.predict(img)[0]
+            return {class_name[i]: float(pred[i]) for i in range(8)}
+
+
+        predict_mamo= predict_img(image)
+        return predict_mamo
+
+    elif model_name == "what kind is":
+        image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
+        im_pil = Image.fromarray(image)
+        pipe = pipeline("image-classification", model="DHEIVER/finetuned-BreastCancer-Classification", device=0)
+
+        def predict(image):
+            result = pipe(image)
+            return {result[i]['label']: float(result[i]['score']) for i in range(2)}
+
+        return predict(im_pil)
+
+
+
+def generate_fixed_size_chart(predictions, image_file, chart_width=6, chart_height=5):
+    # بارگذاری تصویر ماموگرافی
+    mammo_image = plt.imread(image_file)
+
+    # تعداد مدل‌ها
+    num_models = len(predictions)
+
+    # ایجاد figure با تنظیم عرض و ارتفاع هر زیرنمودار
+    fig, axes = plt.subplots(1, num_models + 1, figsize=(chart_width * (num_models + 1), chart_height), constrained_layout=True)
+    # fig.subplots_adjust(wspace=0.7)  # فاصله ثابت بین نمودارها
+
+    # نمایش تصویر ماموگرافی در subplot اول
+    axes[0].imshow(mammo_image, cmap='gray')
+    axes[0].axis('off')
+    axes[0].set_title("Mammogram")
+
+    # ایجاد نمودارهای پیش‌بینی برای هر مدل در subplots بعدی
+    for i, (model_name, prediction) in enumerate(predictions.items(), start=1):
+        labels, values = zip(*prediction.items())
+        axes[i].barh(labels, values, color='skyblue')
+        axes[i].set_xlabel('Probability (%)')
+        axes[i].set_title(f'{model_name}')
+
+    # ذخیره‌ی نمودار در فایل
+    chart_path = f"{os.getcwd()}/{os.path.basename(image_file)}_combined_chart.png"
+    plt.savefig(chart_path, bbox_inches='tight')
+    plt.close(fig)
+
+    return chart_path
+
+def generate_pdf(patient_info, predictions):
+    all_charts = []
+    for image_file, prediction in predictions:
+        chart = generate_fixed_size_chart(prediction, image_file)
+        all_charts.append(chart)
+
+    # تولید محتوای HTML برای PDF
+    html_content = f"""
+    <html>
+    <head>
+        <style>
+            body {{ font-family: Arial, sans-serif; }}
+            h1 {{ color: #2F4F4F; text-align: center; margin-bottom: 30px; }}
+            .info-container {{
+                display: flex;
+                flex-wrap: wrap;
+                justify-content: space-between;
+                margin-bottom: 20px;
+            }}
+            .info-item {{
+                width: 45%;
+                margin-bottom: 10px;
+            }}
+            .image-container {{
+                text-align: center;
+                margin-bottom: 50px;
+            }}
+        </style>
+    </head>
+    <body>
+        <h1>Patient Report</h1>
+        <div class="image-container">
+            <h3>Patient Image:</h3>
+            <img src="{patient_info.get('ImagePath', '')}" alt="Patient Image" width="300">
+        </div>
+        <div class="image-container">
+            <h3>Patient Information:</h3>
+            <div class="info-container">
+                {"".join(f"<div class='info-item'><strong>{key}:</strong> {value if value else '-'}</div>" for key, value in patient_info.items() if key != "ImagePath")}
+            </div>
+        </div>
+        <h3>Predictions:</h3>
+        {"".join(f"<div ><img src='{chart}' width='80%'></div>" for chart in all_charts)}
+    </body>
+    </html>
+    """
+
+    # تنظیمات PDF
+    pdf_path = "patient_report.pdf"
+    config = pdfkit.configuration(wkhtmltopdf='/usr/bin/wkhtmltopdf')
+    options = {
+        "enable-local-file-access": True,
+        "no-stop-slow-scripts": True,
+    }
+    pdfkit.from_string(html_content, pdf_path, configuration=config, options=options)
+
+    return pdf_path
+
+
+
+# تابع نمایش گزارش و تولید PDF
+def display_report(patient_info, predictions):
+    pdf_path = generate_pdf(patient_info, predictions)
+    report_content = f"<h2>Patient Report</h2><p>{patient_info}</p><h2>Predictions</h2>{predictions}"
+    return report_content, pdf_path
+
+# رابط Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("## Breast Cancer Detection - Multi-Model Interface")
+
+    # صفحه اول - اطلاعات بیمار
+    with gr.Tab("Patient Info"):
+        patient_image = gr.Image(label="Upload Patient Profile Image", type="pil")
+        name = gr.Textbox(label="Name")
+        height = gr.Number(label="Height (cm)")
+        weight = gr.Number(label="Weight (kg)")
+        age = gr.Number(label="Age")
+        gender = gr.Radio(["Male", "Female", "Other"], label="Gender")
+        residence = gr.Textbox(label="Residence")
+        birth_place = gr.Textbox(label="Birth Place")
+        occupation = gr.Textbox(label="Occupation")
+        medical_history = gr.Textbox(label="Medical History")
+        patient_info = gr.State()
+        patient_info_submit = gr.Button("Next")
+
+    # صفحه دوم - انتخاب مدل‌ها و آپلود تصاویر ماموگرافی
+    with gr.Tab("Model & Image Selection"):
+        model_choice = gr.CheckboxGroup(["how dense is", "what kind is"], label="Select Model(s)", interactive=True)
+        mammography_images = gr.File(label="Upload Mammography Image(s)", file_count="multiple", type="filepath")
+        predictions = gr.State()
+        process_button = gr.Button("Process Images")
+
+    # صفحه سوم - نمایش اطلاعات و پیش‌بینی
+    with gr.Tab("Results"):
+        report_display = gr.HTML(label="Patient Report")
+        download_button = gr.Button("Download Report")
+
+    # جمع‌آوری اطلاعات بیمار و انتقال به مرحله بعدی
+    def collect_patient_info(image, name, height, weight, age, gender, residence, birth_place, occupation, medical_history):
+        # ذخیره تصویر بیمار و اضافه کردن مسیر به اطلاعات بیمار
+        image_path = "patient_image.jpg"
+        image.save(image_path)
+        return {
+            "Name": name,
+            "Gender": gender,
+            "Height": height,
+            "Weight": weight,
+            "Age": age,
+            "Residence": residence,
+            "Birth Place": birth_place,
+            "Occupation": occupation,
+            "Medical History": medical_history,
+            "ImagePath": image_path  # اضافه کردن مسیر تصویر
+        }
+
+    patient_info_submit.click(
+        collect_patient_info,
+        inputs=[patient_image, name, height, weight, age, gender, residence, birth_place, occupation, medical_history],
+        outputs=patient_info
+    )
+
+    # پردازش تصاویر ماموگرافی با مدل‌های انتخابی
+    def process_images(patient_info, selected_models, images):
+        all_predictions = []
+        for image_file in images:
+            image = Image.open(image_file)
+            image_predictions = {model: predict_demo(image, model) for model in selected_models}
+            all_predictions.append((image_file, image_predictions))
+        return all_predictions
+
+    process_button.click(
+        process_images,
+        inputs=[patient_info, model_choice, mammography_images],
+        outputs=predictions
+    )
+
+    # نمایش گزارش بیمار و پیش‌بینی‌ها در صفحه سوم
+    download_button.click(
+        display_report,
+        inputs=[patient_info, predictions],
+        outputs=[report_display, gr.File(label="Download PDF Report")]  # اصلاح خروجی برای Gradio
+    )
+
+demo.launch(debug=True, share=True)

breast_cancer_project_finally_finished.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "GZR221V_EwiN"
+   },
+   "outputs": [],
+   "source": [
+    "import pdfkit\n",
+    "path_wkhtmltopdf = \"/usr/bin/wkhtmltopdf\"\n",
+    "config = pdfkit.configuration(wkhtmltopdf=path_wkhtmltopdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "e6si8Vop4FjB"
+   },
+   "outputs": [],
+   "source": [
+    "# import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import cv2\n",
+    "import gradio as gr\n",
+    "# from numpy import asarray\n",
+    "from transformers import pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3b-TCKUozaMn",
+    "outputId": "58f44bd4-8916-436d-e00a-1e19a4586dbc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+      "74836368/74836368 [==============================] - 4s 0us/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
+      "  saving_api.save_model(\n",
+      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, BatchNormalization, Dropout,AveragePooling2D\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.applications import DenseNet201\n",
+    "from keras.models import Model\n",
+    "from keras.models import Sequential\n",
+    "from keras.regularizers import *\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 889
+    },
+    "id": "Ylgxw-pHiLCC",
+    "outputId": "76d5f8ed-82f4-4227-ff75-dcbba696eb14"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
+      "* Running on public URL: https://54b3e1140167dae729.gradio.live\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://54b3e1140167dae729.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x78c970190a60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 4s 4s/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x78c97cd316c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 4s 4s/step\n",
+      "1/1 [==============================] - 4s 4s/step\n",
+      "1/1 [==============================] - 3s 3s/step\n",
+      "1/1 [==============================] - 5s 5s/step\n",
+      "1/1 [==============================] - 4s 4s/step\n",
+      "1/1 [==============================] - 5s 5s/step\n",
+      "1/1 [==============================] - 3s 3s/step\n",
+      "1/1 [==============================] - 3s 3s/step\n",
+      "1/1 [==============================] - 4s 4s/step\n",
+      "1/1 [==============================] - 3s 3s/step\n",
+      "1/1 [==============================] - 3s 3s/step\n",
+      "Keyboard interruption in main thread... closing server.\n",
+      "Killing tunnel 127.0.0.1:7860 <> https://54b3e1140167dae729.gradio.live\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import cv2\n",
+    "import gradio as gr\n",
+    "from transformers import pipeline\n",
+    "import pdfkit  # نیاز به نصب دارد (pip install pdfkit)\n",
+    "import os\n",
+    "\n",
+    "# تابع پیش‌بینی\n",
+    "def predict_demo(image, model_name):\n",
+    "    if model_name == \"how dense is\":\n",
+    "        image = np.asarray(image)\n",
+    "\n",
+    "        # مدل اول\n",
+    "        def load_model():\n",
+    "            model = tf.keras.models.load_model(\"/content/model.h5\", compile=False)\n",
+    "            model.compile(optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.00001, decay=0.0001),\n",
+    "                          metrics=[\"accuracy\"], loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1))\n",
+    "            model.load_weights(\"/content/modeldense1.h5\")\n",
+    "            return model\n",
+    "\n",
+    "        model = load_model()\n",
+    "\n",
+    "        def preprocess(image):\n",
+    "            image = cv2.resize(image, (224, 224))\n",
+    "            kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])\n",
+    "            im = cv2.filter2D(image, -1, kernel)\n",
+    "            if im.ndim == 3:\n",
+    "                # اضافه کردن بعد جدید برای ورودی مدل\n",
+    "                im = np.expand_dims(im, axis=0)\n",
+    "            elif im.ndim == 2:\n",
+    "                # اگر تصویر سیاه و سفید باشد\n",
+    "                im = np.expand_dims(im, axis=-1)\n",
+    "                im = np.repeat(im, 3, axis=-1)\n",
+    "                im = np.expand_dims(im, axis=0)\n",
+    "            return im\n",
+    "\n",
+    "\n",
+    "        class_name = ['Benign with Density=1', 'Malignant with Density=1', 'Benign with Density=2',\n",
+    "                      'Malignant with Density=2', 'Benign with Density=3', 'Malignant with Density=3',\n",
+    "                      'Benign with Density=4', 'Malignant with Density=4']\n",
+    "\n",
+    "        def predict_img(img):\n",
+    "            img = preprocess(img)\n",
+    "            img = img / 255.0\n",
+    "            pred = model.predict(img)[0]\n",
+    "            return {class_name[i]: float(pred[i]) for i in range(8)}\n",
+    "\n",
+    "\n",
+    "        predict_mamo= predict_img(image)\n",
+    "        return predict_mamo\n",
+    "\n",
+    "    elif model_name == \"what kind is\":\n",
+    "        image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)\n",
+    "        im_pil = Image.fromarray(image)\n",
+    "        pipe = pipeline(\"image-classification\", model=\"DHEIVER/finetuned-BreastCancer-Classification\", device=0)\n",
+    "\n",
+    "        def predict(image):\n",
+    "            result = pipe(image)\n",
+    "            return {result[i]['label']: float(result[i]['score']) for i in range(2)}\n",
+    "\n",
+    "        return predict(im_pil)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def generate_fixed_size_chart(predictions, image_file, chart_width=6, chart_height=5):\n",
+    "    # بارگذاری تصویر ماموگرافی\n",
+    "    mammo_image = plt.imread(image_file)\n",
+    "\n",
+    "    # تعداد مدل‌ها\n",
+    "    num_models = len(predictions)\n",
+    "\n",
+    "    # ایجاد figure با تنظیم عرض و ارتفاع هر زیرنمودار\n",
+    "    fig, axes = plt.subplots(1, num_models + 1, figsize=(chart_width * (num_models + 1), chart_height), constrained_layout=True)\n",
+    "    # fig.subplots_adjust(wspace=0.7)  # فا��له ثابت بین نمودارها\n",
+    "\n",
+    "    # نمایش تصویر ماموگرافی در subplot اول\n",
+    "    axes[0].imshow(mammo_image, cmap='gray')\n",
+    "    axes[0].axis('off')\n",
+    "    axes[0].set_title(\"Mammogram\")\n",
+    "\n",
+    "    # ایجاد نمودارهای پیش‌بینی برای هر مدل در subplots بعدی\n",
+    "    for i, (model_name, prediction) in enumerate(predictions.items(), start=1):\n",
+    "        labels, values = zip(*prediction.items())\n",
+    "        axes[i].barh(labels, values, color='skyblue')\n",
+    "        axes[i].set_xlabel('Probability (%)')\n",
+    "        axes[i].set_title(f'{model_name}')\n",
+    "\n",
+    "    # ذخیره‌ی نمودار در فایل\n",
+    "    chart_path = f\"/content/{os.path.basename(image_file)}_combined_chart.png\"\n",
+    "    plt.savefig(chart_path, bbox_inches='tight')\n",
+    "    plt.close(fig)\n",
+    "\n",
+    "    return chart_path\n",
+    "\n",
+    "def generate_pdf(patient_info, predictions):\n",
+    "    all_charts = []\n",
+    "    for image_file, prediction in predictions:\n",
+    "        chart = generate_fixed_size_chart(prediction, image_file)\n",
+    "        all_charts.append(chart)\n",
+    "\n",
+    "    # تولید محتوای HTML برای PDF\n",
+    "    html_content = f\"\"\"\n",
+    "    <html>\n",
+    "    <head>\n",
+    "        <style>\n",
+    "            body {{ font-family: Arial, sans-serif; }}\n",
+    "            h1 {{ color: #2F4F4F; text-align: center; margin-bottom: 30px; }}\n",
+    "            .info-container {{\n",
+    "                display: flex;\n",
+    "                flex-wrap: wrap;\n",
+    "                justify-content: space-between;\n",
+    "                margin-bottom: 20px;\n",
+    "            }}\n",
+    "            .info-item {{\n",
+    "                width: 45%;\n",
+    "                margin-bottom: 10px;\n",
+    "            }}\n",
+    "            .image-container {{\n",
+    "                text-align: center;\n",
+    "                margin-bottom: 50px;\n",
+    "            }}\n",
+    "        </style>\n",
+    "    </head>\n",
+    "    <body>\n",
+    "        <h1>Patient Report</h1>\n",
+    "        <div class=\"image-container\">\n",
+    "            <h3>Patient Image:</h3>\n",
+    "            <img src=\"{patient_info.get('ImagePath', '')}\" alt=\"Patient Image\" width=\"300\">\n",
+    "        </div>\n",
+    "        <div class=\"image-container\">\n",
+    "            <h3>Patient Information:</h3>\n",
+    "            <div class=\"info-container\">\n",
+    "                {\"\".join(f\"<div class='info-item'><strong>{key}:</strong> {value if value else '-'}</div>\" for key, value in patient_info.items() if key != \"ImagePath\")}\n",
+    "            </div>\n",
+    "        </div>\n",
+    "        <h3>Predictions:</h3>\n",
+    "        {\"\".join(f\"<div ><img src='{chart}' width='80%'></div>\" for chart in all_charts)}\n",
+    "    </body>\n",
+    "    </html>\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # تنظیمات PDF\n",
+    "    pdf_path = \"/content/patient_report.pdf\"\n",
+    "    config = pdfkit.configuration(wkhtmltopdf='/usr/bin/wkhtmltopdf')\n",
+    "    options = {\n",
+    "        \"enable-local-file-access\": True,\n",
+    "        \"no-stop-slow-scripts\": True,\n",
+    "    }\n",
+    "    pdfkit.from_string(html_content, pdf_path, configuration=config, options=options)\n",
+    "\n",
+    "    return pdf_path\n",
+    "\n",
+    "\n",
+    "\n",
+    "# تابع نمایش گزارش و تولید PDF\n",
+    "def display_report(patient_info, predictions):\n",
+    "    pdf_path = generate_pdf(patient_info, predictions)\n",
+    "    report_content = f\"<h2>Patient Report</h2><p>{patient_info}</p><h2>Predictions</h2>{predictions}\"\n",
+    "    return report_content, pdf_path\n",
+    "\n",
+    "# رابط Gradio\n",
+    "with gr.Blocks() as demo:\n",
+    "    gr.Markdown(\"## Breast Cancer Detection - Multi-Model Interface\")\n",
+    "\n",
+    "    # صفحه اول - اطلاعات بیمار\n",
+    "    with gr.Tab(\"Patient Info\"):\n",
+    "        patient_image = gr.Image(label=\"Upload Patient Profile Image\", type=\"pil\")\n",
+    "        name = gr.Textbox(label=\"Name\")\n",
+    "        height = gr.Number(label=\"Height (cm)\")\n",
+    "        weight = gr.Number(label=\"Weight (kg)\")\n",
+    "        age = gr.Number(label=\"Age\")\n",
+    "        gender = gr.Radio([\"Male\", \"Female\", \"Other\"], label=\"Gender\")\n",
+    "        residence = gr.Textbox(label=\"Residence\")\n",
+    "        birth_place = gr.Textbox(label=\"Birth Place\")\n",
+    "        occupation = gr.Textbox(label=\"Occupation\")\n",
+    "        medical_history = gr.Textbox(label=\"Medical History\")\n",
+    "        patient_info = gr.State()\n",
+    "        patient_info_submit = gr.Button(\"Next\")\n",
+    "\n",
+    "    # صفحه دوم - انتخاب مدل‌ها و آپلود تصاویر ماموگرافی\n",
+    "    with gr.Tab(\"Model & Image Selection\"):\n",
+    "        model_choice = gr.CheckboxGroup([\"how dense is\", \"what kind is\"], label=\"Select Model(s)\", interactive=True)\n",
+    "        mammography_images = gr.File(label=\"Upload Mammography Image(s)\", file_count=\"multiple\", type=\"filepath\")\n",
+    "        predictions = gr.State()\n",
+    "        process_button = gr.Button(\"Process Images\")\n",
+    "\n",
+    "    # صفحه سوم - نمایش اطلاعات و پیش‌بینی\n",
+    "    with gr.Tab(\"Results\"):\n",
+    "        report_display = gr.HTML(label=\"Patient Report\")\n",
+    "        download_button = gr.Button(\"Download Report\")\n",
+    "\n",
+    "    # جمع‌آوری اطلاعات بیمار و انتقال به مرحله بعدی\n",
+    "    def collect_patient_info(image, name, height, weight, age, gender, residence, birth_place, occupation, medical_history):\n",
+    "        # ذخیره تصویر بیمار و اضافه کردن مسیر به اطلاعات بیمار\n",
+    "        image_path = \"/content/patient_image.jpg\"\n",
+    "        image.save(image_path)\n",
+    "        return {\n",
+    "            \"Name\": name,\n",
+    "            \"Gender\": gender,\n",
+    "            \"Height\": height,\n",
+    "            \"Weight\": weight,\n",
+    "            \"Age\": age,\n",
+    "            \"Residence\": residence,\n",
+    "            \"Birth Place\": birth_place,\n",
+    "            \"Occupation\": occupation,\n",
+    "            \"Medical History\": medical_history,\n",
+    "            \"ImagePath\": image_path  # اضافه کردن مسیر تصویر\n",
+    "        }\n",
+    "\n",
+    "    patient_info_submit.click(\n",
+    "        collect_patient_info,\n",
+    "        inputs=[patient_image, name, height, weight, age, gender, residence, birth_place, occupation, medical_history],\n",
+    "        outputs=patient_info\n",
+    "    )\n",
+    "\n",
+    "    # پردازش تصاویر ماموگرافی با مدل‌های انتخابی\n",
+    "    def process_images(patient_info, selected_models, images):\n",
+    "        all_predictions = []\n",
+    "        for image_file in images:\n",
+    "            image = Image.open(image_file)\n",
+    "            image_predictions = {model: predict_demo(image, model) for model in selected_models}\n",
+    "            all_predictions.append((image_file, image_predictions))\n",
+    "        return all_predictions\n",
+    "\n",
+    "    process_button.click(\n",
+    "        process_images,\n",
+    "        inputs=[patient_info, model_choice, mammography_images],\n",
+    "        outputs=predictions\n",
+    "    )\n",
+    "\n",
+    "    # نمایش گزارش بیمار و پیش‌بینی‌ها در صفحه سوم\n",
+    "    download_button.click(\n",
+    "        display_report,\n",
+    "        inputs=[patient_info, predictions],\n",
+    "        outputs=[report_display, gr.File(label=\"Download PDF Report\")]  # اصلاح خروجی برای Gradio\n",
+    "    )\n",
+    "\n",
+    "demo.launch(debug=True, share=True)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

requirement.txt

@@ -1,11 +1,11 @@
-tensorflow==2.15.1
-gradio
-diffusers
-accelerate
-git+https://github.com/TencentARC/PhotoMaker.git
-pdfkit
-wkhtmltopdf
-transformers
-numpy
-pandas
+tensorflow==2.15.1
+gradio
+diffusers
+accelerate
+git+https://github.com/TencentARC/PhotoMaker.git
+pdfkit
+wkhtmltopdf
+transformers
+numpy
+pandas
 matplotlib

setup.sh

@@ -1,2 +1,2 @@
-#!/bin/bash
+#!/bin/bash
 apt-get update && apt-get install -y wkhtmltopdf