Update app.py

app.py

@@ -4,6 +4,8 @@ import numpy as np
 from tensorflow.keras.preprocessing import image
 from PIL import Image
 import os
+from fpdf import FPDF
+import datetime
 
 # Load the trained model
 model = tf.keras.models.load_model("my_keras_model.h5")
@@ -21,45 +23,60 @@ def predict_image(img):
     # Assuming binary classification (fractured or normal)
     class_names = ['Fractured', 'Normal']
     predicted_class = class_names[int(prediction[0] > 0.5)]  # Threshold at 0.5
+    confidence = prediction[0][0]
 
-    return f"Prediction: {predicted_class} (Confidence: {prediction[0][0]:.2f})"
+    return predicted_class, confidence
 
-# Get image paths dynamically
-sample_images_dir = "samples"
-sample_images = [os.path.join(sample_images_dir, f) for f in os.listdir(sample_images_dir) if f.endswith(('.jpg', '.png'))]
+# Function to generate a PDF report
+def generate_report(name, age, weight, height, img):
+    # Predict result
+    predicted_class, confidence = predict_image(img)
 
-# File download paths
-report_path = "https://huggingface.co/spaces/ftx7go/bone-fracture-detection-model/resolve/main/files/report.pdf"
-notebook_path = "https://huggingface.co/spaces/ftx7go/bone-fracture-detection-model/resolve/main/files/bfd.ipynb"
+    # Create PDF
+    pdf = FPDF()
+    pdf.set_auto_page_break(auto=True, margin=15)
+    pdf.add_page()
+    pdf.set_font("Arial", size=12)
 
-# Function to provide file download links
-def get_download_links():
-    return f"""  
-    **Download Project Files:**  
-    - [Download Report (PDF)]({report_path})  
-    - [Download Notebook (IPYNB)]({notebook_path})  
-    """
+    # Add title
+    pdf.set_font("Arial", style='B', size=16)
+    pdf.cell(200, 10, "Bone Fracture Detection Report", ln=True, align='C')
+    pdf.ln(10)
+
+    # Add patient details
+    pdf.set_font("Arial", size=12)
+    pdf.cell(200, 10, f"Patient Name: {name}", ln=True)
+    pdf.cell(200, 10, f"Age: {age}", ln=True)
+    pdf.cell(200, 10, f"Weight: {weight} kg", ln=True)
+    pdf.cell(200, 10, f"Height: {height} cm", ln=True)
+    pdf.cell(200, 10, f"Diagnosis Date: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True)
+    pdf.ln(10)
+
+    # Add prediction result
+    pdf.set_font("Arial", style='B', size=14)
+    pdf.cell(200, 10, f"Diagnosis: {predicted_class}", ln=True)
+    pdf.set_font("Arial", size=12)
+    pdf.cell(200, 10, f"Confidence: {confidence:.2f}", ln=True)
+
+    # Save PDF
+    pdf_filename = "patient_report.pdf"
+    pdf.output(pdf_filename)
+
+    return pdf_filename
 
 # Define Gradio Interface
 interface = gr.Interface(
-    fn=predict_image,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Textbox(),
-    examples=sample_images,  # Preloaded images for testing
-    title="Bone Fracture Detection",
-    description="Upload an X-ray image or select a sample image to check for fractures.",
-    article=f"""
-    ## Instructions
-    - Upload an X-ray image to check for fractures.
-    - Select a sample image for quick testing.
-    - The model will predict if the bone is fractured or normal.
-    
-    ## Capabilities
-    - Detects bone fractures from X-ray images.
-    - Uses a trained deep learning model for classification.
-    
-    {get_download_links()}  
-    """
+    fn=generate_report,
+    inputs=[
+        gr.Textbox(label="Patient Name"),
+        gr.Number(label="Age"),
+        gr.Number(label="Weight (kg)"),
+        gr.Number(label="Height (cm)"),
+        gr.Image(type="pil", label="X-ray Image")
+    ],
+    outputs=gr.File(label="Download Report"),
+    title="Bone Fracture Detection & Diagnosis",
+    description="Fill in the patient details and upload an X-ray image. The system will analyze the image and generate a PDF report with the diagnosis."
 )
 
 # Launch the Gradio app