Create app.py

app.py

@@ -0,0 +1,127 @@
+# Install required packages
+#!pip install gradio tensorflow opencv-python scikit-learn
+
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import cv2
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from tensorflow.keras.preprocessing.image import img_to_array
+from sklearn.preprocessing import StandardScaler
+import pandas as pd
+
+class SurgicalAssistSystem:
+    def __init__(self):
+        # Initialize the image processing model (ResNet50 pretrained)
+        self.image_model = ResNet50(weights='imagenet', include_top=False,
+                                  input_shape=(224, 224, 3))
+        
+        # Initialize scaler for vital signs
+        self.scaler = StandardScaler()
+        
+        # Define normal ranges for vital signs
+        self.vital_ranges = {
+            'heart_rate': (60, 100),
+            'blood_pressure_systolic': (90, 140),
+            'blood_pressure_diastolic': (60, 90),
+            'oxygen_saturation': (95, 100),
+            'temperature': (36.5, 37.5)
+        }
+
+    def preprocess_image(self, image):
+        # Resize image to expected dimensions
+        image = cv2.resize(image, (224, 224))
+        image = img_to_array(image)
+        image = np.expand_dims(image, axis=0)
+        image = preprocess_input(image)
+        return image
+
+    def analyze_image(self, image):
+        # Preprocess and analyze the image
+        processed_image = self.preprocess_image(image)
+        features = self.image_model.predict(processed_image)
+        
+        # Simplified analysis - detecting potential anomalies
+        feature_mean = np.mean(features)
+        if feature_mean > 0.5:
+            return "Potential anomaly detected in the surgical field"
+        else:
+            return "No immediate concerns in the surgical field"
+
+    def analyze_vitals(self, vitals_dict):
+        alerts = []
+        for vital, value in vitals_dict.items():
+            if vital in self.vital_ranges:
+                min_val, max_val = self.vital_ranges[vital]
+                if value < min_val:
+                    alerts.append(f"Warning: {vital} is below normal range")
+                elif value > max_val:
+                    alerts.append(f"Warning: {vital} is above normal range")
+        
+        if not alerts:
+            return "All vital signs are within normal ranges"
+        return "\n".join(alerts)
+
+    def generate_recommendations(self, image_analysis, vitals_analysis):
+        recommendations = []
+        
+        if "anomaly" in image_analysis.lower():
+            recommendations.append("- Recommend detailed inspection of highlighted area")
+            recommendations.append("- Consider additional imaging")
+        
+        if "warning" in vitals_analysis.lower():
+            recommendations.append("- Monitor vital signs closely")
+            recommendations.append("- Consider adjusting procedure parameters")
+        
+        if not recommendations:
+            recommendations.append("- Proceed with standard protocol")
+            recommendations.append("- Continue routine monitoring")
+        
+        return "\n".join(recommendations)
+
+# Initialize the system
+system = SurgicalAssistSystem()
+
+def process_surgical_data(image, heart_rate, blood_pressure_systolic, 
+                         blood_pressure_diastolic, oxygen_saturation, temperature):
+    # Convert image to numpy array if it's not already
+    if isinstance(image, str):
+        return "Please provide a valid image"
+    
+    # Create vitals dictionary
+    vitals = {
+        'heart_rate': heart_rate,
+        'blood_pressure_systolic': blood_pressure_systolic,
+        'blood_pressure_diastolic': blood_pressure_diastolic,
+        'oxygen_saturation': oxygen_saturation,
+        'temperature': temperature
+    }
+    
+    # Analyze image and vitals
+    image_analysis = system.analyze_image(image)
+    vitals_analysis = system.analyze_vitals(vitals)
+    recommendations = system.generate_recommendations(image_analysis, vitals_analysis)
+    
+    return (f"Image Analysis:\n{image_analysis}\n\n"
+            f"Vitals Analysis:\n{vitals_analysis}\n\n"
+            f"Recommendations:\n{recommendations}")
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=process_surgical_data,
+    inputs=[
+        gr.Image(label="Surgical Field Image"),
+        gr.Number(label="Heart Rate (bpm)"),
+        gr.Number(label="Blood Pressure - Systolic (mmHg)"),
+        gr.Number(label="Blood Pressure - Diastolic (mmHg)"),
+        gr.Number(label="Oxygen Saturation (%)"),
+        gr.Number(label="Temperature (°C)")
+    ],
+    outputs=gr.Textbox(label="Analysis Results"),
+    title="Surgical Assistance System",
+    description="Upload an image of the surgical field and enter vital signs for analysis and recommendations."
+)
+
+# Launch the interface
+iface.launch(share=True)