update main.py and write to retrive live feed from cam module

main.py

 import json
 import cv2
 import numpy as np
+import logging
 from fastapi import FastAPI, WebSocket
 from keras.models import load_model
 from keras.utils import img_to_array
 
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
 # Initialize the FastAPI app
 app = FastAPI()
 
@@ -77,10 +81,13 @@ async def websocket_endpoint(websocket: WebSocket):
                     }
                 })
 
+                logging.info(f"Detected emotions: {emotions}")
+
             # Send the emotion predictions back to the client
             await websocket.send_text(json.dumps(emotions))
 
     except Exception as e:
+        logging.error(f"An error occurred: {e}")
         await websocket.close()
 
 if __name__ == "__main__":

main_test.py

+import asyncio
+import json
+import traceback
+import cv2
+import numpy as np
+import logging
+from fastapi import FastAPI, WebSocket
+from keras.models import load_model
+from keras.utils import img_to_array
+import urllib.request
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Initialize the FastAPI app
+app = FastAPI()
+
+# Load the model
+emotion_model = load_model('emotion_model.keras')
+
+# Load class indices used during training
+with open('class_indices.json', 'r') as f:
+    class_indices = json.load(f)
+
+# Convert indices back to labels
+emotion_labels = [None] * len(class_indices)
+for class_name, index in class_indices.items():
+    emotion_labels[index] = class_name
+
+# Load the haarcascade file for face detection
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# Replace with your IP camera stream URL
+url = 'http://192.168.1.7/cam-hi.jpg'
+
+
+@app.websocket("/ws/emotion")
+async def websocket_endpoint(websocket: WebSocket):
+    await websocket.accept()
+    try:
+        while True:
+            try:
+                # Capture frame from the IP camera stream
+                img_resp = urllib.request.urlopen(url)
+                imgnp = np.array(bytearray(img_resp.read()), dtype=np.uint8)
+                frame = cv2.imdecode(imgnp, cv2.IMREAD_COLOR)
+
+                # Flip the image vertically and horizontally (flipCode=-1)
+                flipped_frame = cv2.flip(frame, -1)
+
+                # Convert color to grayscale
+                gray = cv2.cvtColor(flipped_frame, cv2.COLOR_BGR2GRAY)
+
+                # Perform face detection
+                faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+                emotions = []
+                for (x, y, w, h) in faces:
+                    # Extract grayscale face ROI
+                    face_gray = gray[y:y + h, x:x + w]
+
+                    # Resize the image to 48x48 for the model
+                    face_gray = cv2.resize(face_gray, (48, 48))
+
+                    # Convert image to array
+                    img = img_to_array(face_gray)
+
+                    # Reshape image into format for model
+                    img = np.expand_dims(img, axis=0)
+
+                    # Normalize the image
+                    img /= 255
+
+                    # Get the prediction from the model
+                    prediction = emotion_model.predict(img)
+
+                    # Get the index of the highest predicted value
+                    max_index = np.argmax(prediction[0])
+
+                    # Get the label corresponding to the prediction
+                    emotion_prediction = emotion_labels[max_index]
+
+                    emotions.append({
+                        "emotion": emotion_prediction,
+                        "bounding_box": {
+                            "x": int(x),
+                            "y": int(y),
+                            "width": int(w),
+                            "height": int(h)
+                        }
+                    })
+
+                logging.info(f"Detected emotions: {emotions}")
+
+                # Send the emotion predictions back to the client
+                await websocket.send_text(json.dumps(emotions))
+
+            except Exception as e:
+                logging.error(f"Error processing frame: {e}")
+                traceback.print_exc()
+                await websocket.close()
+
+            await asyncio.sleep(1)
+
+    except Exception as e:
+        logging.error(f"WebSocket error: {e}")
+        traceback.print_exc()
+        await websocket.close()
+    finally:
+        logging.info("Closing WebSocket connection")
+        await websocket.close()
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

+fastapi==0.112.1
+uvicorn==0.30.6
+absl-py==2.1.0
+appdirs==1.4.4
+astunparse==1.6.3
+atomicwrites==1.4.0
+backports.weakref==1.0.post1
+bkcharts==0.2
+black==19.10b0
+brotlipy==0.7.0
+certifi==2021.10.8
+comtypes==1.1.10
+constantly==15.1.0
+cssselect==1.1.0
+cytoolz==0.11.0
+daal==2021.4.0
+daal4py==2021.5.0
+datashape
+et-xmlfile==1.1.0
+flatbuffers==24.3.25
+fonttools==4.25.0
+gast==0.5.4
+google-pasta==0.2.0
+graphviz==0.20.3
+grpcio
+h5py==3.11.0
+inflection==0.5.1
+keras==3.2.1
+markdown-it-py==3.0.0
+mccabe==0.6.1
+mdurl==0.1.2
+ml-dtypes==0.3.2
+mpmath==1.2.1
+munkres==1.1.4
+mypy-extensions==0.4.3
+namex==0.0.8
+numpy==1.23.4
+opencv-python==4.9.0.80
+opt-einsum==3.3.0
+optree==0.11.0
+pathspec==0.7.0
+patsy==0.5.2
+pep8==1.7.1
+Pillow==9.0.1
+protobuf==4.25.3
+pyasn1-modules==0.2.8
+pycosat==0.6.3
+pycurl==7.44.1
+PyDispatcher==2.0.5
+pydot==2.0.0
+Pygments==2.17.2
+pyls-spyder==0.4.0
+pyparsing==3.1.2
+pyreadline==2.1
+pytest==7.1.1
+python-lsp-jsonrpc==1.0.0
+python-lsp-server==1.2.4
+pytz==2021.3
+pywin32==302
+PyYAML==6.0
+queuelib==1.5.0
+rich==13.7.1
+scikit-learn-intelex==2021.20220215.102710
+scipy==1.13.0
+sip==4.19.13
+statsmodels==0.13.2
+tables==3.6.1
+tabulate==0.8.9
+tbb==2021.12.0
+tensorboard==2.16.2
+tensorboard-data-server==0.7.2
+tensorflow==2.16.1
+tensorflow-intel==2.16.1
+tensorflow-io-gcs-filesystem==0.31.0
+termcolor==2.4.0
+typing_extensions==4.11.0
+webencodings==0.5.1
+win-unicode-console==0.5
+wincertstore==0.2
+xlwings==0.24.9
+zict==2.0.0

test_cam_stream.py

+import cv2
+import urllib.request
+import numpy as np
+
+# Replace the URL with the IP camera's stream URL
+url = 'http://192.168.1.7/cam-hi.jpg'
+cv2.namedWindow("live Cam Testing", cv2.WINDOW_AUTOSIZE)
+
+# Create a VideoCapture object
+cap = cv2.VideoCapture(url)
+
+# Check if the IP camera stream is opened successfully
+if not cap.isOpened():
+    print("Failed to open the IP camera stream")
+    exit()
+
+# Read and display video frames
+while True:
+    # Read a frame from the video stream
+    img_resp = urllib.request.urlopen(url)
+    imgnp = np.array(bytearray(img_resp.read()), dtype=np.uint8)
+    # ret, frame = cap.read()
+    im = cv2.imdecode(imgnp, -1)
+
+    # Flip the image vertically and horizontally (flipCode=-1)
+    flipped_im = cv2.flip(im, -1)
+
+    cv2.imshow('live Cam Testing', flipped_im)
+    key = cv2.waitKey(5)
+    if key == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()
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