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Clinical_decision_aid_mammogram

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aminahmadi0101 commited on Jan 6

Commit

fe23f81

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1 Parent(s): 14a1af7

Update app.py

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Files changed (1) hide show

app.py +41 -45

app.py CHANGED Viewed

@@ -36,10 +36,10 @@ def predict_demo(image, model_name):
         # 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()
@@ -76,7 +76,7 @@ def predict_demo(image, model_name):
     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")
         def predict(image):
             result = pipe(image)
@@ -110,22 +110,18 @@ def generate_fixed_size_chart(predictions, image_file, chart_width=6, chart_heig
         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
-# استفاده از pdfkit برای تولید PDF
 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)
-    # تبدیل مسیر نسبی به مسیر مطلق
-    image_path = str(Path(patient_info.get('ImagePath', '')).resolve())
     # تولید محتوای HTML برای PDF
     html_content = f"""
     <html>
@@ -155,7 +151,7 @@ def generate_pdf(patient_info, predictions):
         <h1>Patient Report</h1>
         <div class="image-container">
             <h3>Patient Image:</h3>
-            <img src="{image_path}" alt="Patient Image" style="width: 300px; height: auto;">
         </div>
         <div class="image-container">
             <h3>Patient Information:</h3>
@@ -170,17 +166,11 @@ def generate_pdf(patient_info, predictions):
     """
     # # 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)
     html = HTML(string= html_content, base_url="/app")
     html.write_pdf(pdf_path)
     return pdf_path
@@ -188,18 +178,34 @@ def generate_pdf(patient_info, predictions):
 # 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")
-    tab_index = gr.State(0)  # انتخاب تب پیش‌فرض
     # صفحه اول - اطلاعات بیمار
-    with gr.Tabs("Patient Info") as tabs:
         patient_image = gr.Image(label="Upload Patient Profile Image", type="pil")
         name = gr.Textbox(label="Name")
         height = gr.Number(label="Height (cm)")
@@ -211,24 +217,28 @@ with gr.Blocks() as demo:
         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") as tabs2:
         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") as tabs3:
         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,
@@ -241,37 +251,23 @@ with gr.Blocks() as demo:
             "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
-    ).then(
-        lambda: gr.update(visible=True),  # انتقال به تب دوم
-        inputs=[],
-        outputs=tabs2
     )
-    # 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
-    ).then(
-        lambda: gr.update(visible=True),  # انتقال به تب دوم
-        inputs=[],
-        outputs=tabs3
     )
     # نمایش گزارش بیمار و پیش‌بینی‌ها در صفحه سوم
     download_button.click(
         display_report,
@@ -279,4 +275,4 @@ with gr.Blocks() as demo:
         outputs=[report_display, gr.File(label="Download PDF Report")]  # اصلاح خروجی برای Gradio
     )
-demo.launch(debug=True)

         # مدل اول
         def load_model():
+            model = tf.keras.models.load_model("/content/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("/content/modeldense1.h5")
             return model
         model = load_model()
     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)
         axes[i].set_title(f'{model_name}')
     # ذخیره‌ی نمودار در فایل
+    chart_path = f"/content/{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>
         <h1>Patient Report</h1>
         <div class="image-container">
             <h3>Patient Image:</h3>
+            <img src="{patient_info.get('ImagePath', '')}" alt="Patient Image" style="width: 300px; height: auto;">
         </div>
         <div class="image-container">
             <h3>Patient Information:</h3>
     """
     # # تنظیمات PDF
+    pdf_path = "/content/patient_report.pdf"
     html = HTML(string= html_content, base_url="/app")
     html.write_pdf(pdf_path)
     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
+# پردازش تصاویر ماموگرافی با مدل‌های انتخابی
+def process_images(patient_info, selected_models, images):
+    all_predictions = []
+    total_images = len(images)
+    # حلقه برای پردازش تصاویر
+    for idx, image_file in enumerate(images):
+        # به‌روزرسانی وضعیت پردازش
+        status = f"Processing Image {idx + 1} of {total_images}..."
+        yield all_predictions, status  # خروجی موقت
+        image = Image.open(image_file)
+        image_predictions = {model: predict_demo(image, model) for model in selected_models}
+        all_predictions.append((image_file, image_predictions))
+    # پس از اتمام پردازش
+    final_status = "Processing Complete!"
+    yield all_predictions, final_status  # خروجی نهایی
 # 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)")
         occupation = gr.Textbox(label="Occupation")
         medical_history = gr.Textbox(label="Medical History")
         patient_info = gr.State()
+        snackbar = gr.HTML("<div style='color: green; font-weight: bold;'> Patient information was saved </div>", visible=False)  # پیام snackbar
+        patient_info_submit = gr.Button("Save")
     # 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")
+        # المان برای نمایش وضعیت پردازش
+        status_box = gr.Textbox(label="Processing Status", interactive=False)
     # 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 = "/content/patient_image.jpg"
         image.save(image_path)
         return {
             "Name": name,
             "Occupation": occupation,
             "Medical History": medical_history,
             "ImagePath": image_path  # اضافه کردن مسیر تصویر
+        }, gr.update(visible=True)  # نمایش snackbar و مخفی شدن آن پس از 3 ثانیه
     patient_info_submit.click(
         collect_patient_info,
         inputs=[patient_image, name, height, weight, age, gender, residence, birth_place, occupation, medical_history],
+        outputs=[patient_info, snackbar]  # نمایش اطلاعات و snackbar
     )
     process_button.click(
         process_images,
         inputs=[patient_info, model_choice, mammography_images],
+        outputs=[predictions, status_box]  # دو خروجی تنظیم شده است
     )
     # نمایش گزارش بیمار و پیش‌بینی‌ها در صفحه سوم
     download_button.click(
         display_report,
         outputs=[report_display, gr.File(label="Download PDF Report")]  # اصلاح خروجی برای Gradio
     )
+demo.launch(debug=True, share=True)