edit to flask

app.py

@@ -1,22 +1,48 @@
-from flask import Flask, request, jsonify
-import joblib
-from PIL import Image
+import streamlit as st
 import numpy as np
+from PIL import Image
+import joblib
+from flask import Flask, request, jsonify
+from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
+import cv2
+import threading
 
-app = Flask(__name__)
-
-# 加载模型
-model = joblib.load('skin_cancer_model.pkl')
+# 加载训练好的模型
+model_file_path = 'skin_cancer_model.pkl'
+model = joblib.load(model_file_path)
 
-# 图像预处理
+# 图像预处理函数
 def preprocess_image(image):
-    size = (224, 224)
+    size = (224, 224)  # 模型输入尺寸
     image = image.resize(size)
     image_array = np.array(image) / 255.0
     image_flattened = image_array.flatten()
     return [image_flattened]
 
-# 接收图像并分析
+# 定义视频转换器类，处理视频帧
+class VideoTransformer(VideoTransformerBase):
+    def transform(self, frame):
+        img = frame.to_ndarray(format="bgr24")
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+        # 预处理图像
+        img_resized = cv2.resize(img, (224, 224))
+        img_resized = img_resized.astype("float32") / 255.0
+        img_resized = np.expand_dims(img_resized, axis=0)
+
+        # 模型预测
+        prediction = model.predict(img_resized)
+        predicted_class = np.argmax(prediction, axis=1)[0]
+
+        # 在 Streamlit 中显示预测结果
+        st.write(f"Skin Cancer Prediction (Video): {predicted_class}")
+
+        return img
+
+# Flask 应用设置
+app = Flask(__name__)
+
+# 接收图像并通过模型分析
 @app.route('/analyze', methods=['POST'])
 def analyze():
     file = request.files['image']
@@ -27,5 +53,54 @@ def analyze():
     result = class_names[prediction]
     return jsonify({"result": result})
 
-if __name__ == "__main__":
-    app.run()
+# 运行 Flask 应用
+def run_flask():
+    app.run(host='0.0.0.0', port=8000)
+
+# 主程序 - Streamlit UI 和交互逻辑
+def main():
+    st.title("Skin Diagnosis")
+
+    st.write(
+        "Please note that this app is not 100% accurate. If your result happens to be malignant, please contact a medical professional for further instructions.")
+
+    # 实时视频诊断
+    st.header("Real-Time Video Diagnosis")
+    webrtc_streamer(key="example", video_processor_factory=VideoTransformer)
+
+    # 图像上传与预测
+    st.header("Image Upload Diagnosis")
+    st.write("**Upload an image to detect if it's benign or malignant:**")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+        # 预处理图像
+        processed_image = preprocess_image(image)
+
+        # 模型预测
+        prediction = model.predict([processed_image])[0]
+        class_names = {0: 'Benign', 1: 'Malignant'}
+        result = class_names[prediction]
+
+        st.write(f"Skin Cancer Prediction (Image): {result}")
+
+    # 阳光暴露选项
+    st.write("**Have you had a large amount of sun exposure recently?**")
+    result = st.button("Yes")
+    if result:
+        st.write(
+            "Please note that high levels of sun exposure can increase the risk of skin cancer. Be cautious and consider regular skin check-ups.")
+
+# 使用线程并行运行 Flask 和 Streamlit
+if __name__ == '__main__':
+    # 启动 Flask 服务器线程
+    flask_thread = threading.Thread(target=run_flask)
+    flask_thread.daemon = True
+    flask_thread.start()
+
+    # 运行 Streamlit 前端
+    main()

requirements.txt

@@ -1,29 +1,38 @@
 # Streamlit for the frontend interface
-streamlit==1.15.2
+streamlit
 
 # Flask for backend API
-Flask==2.1.0  # 版本与 Werkzeug 兼容
+Flask
 
 # Werkzeug for Flask compatibility
-Werkzeug==2.1.2  # 与 Flask 2.1.0 兼容的版本
+Werkzeug
 
 # Joblib for loading and saving models
-joblib==1.1.0
+joblib
 
 # Numpy for numerical computations
-numpy==1.21.6
+numpy
 
 # Scipy for scientific computing (required by scikit-learn)
-scipy==1.7.3
+scipy
 
 # Scikit-learn for machine learning model
-scikit-learn==1.0.2
+scikit-learn
 
 # PIL (Python Imaging Library) for image processing
-Pillow==9.0.1
+Pillow
 
 # OpenCV for image and video processing
-opencv-python-headless==4.5.5.62
+opencv-python-headless
 
 # Streamlit WebRTC for video streaming
-streamlit-webrtc==0.30.0
+streamlit-webrtc
+
+# (Optional) Flask-CORS for handling CORS requests in Flask
+Flask-Cors
+
+# (Optional) Requests for sending HTTP requests
+requests
+
+# (Optional) Python-dotenv for managing environment variables
+python-dotenv