diff --git a/prediction_service/__pycache__/views.cpython-310.pyc b/prediction_service/__pycache__/views.cpython-310.pyc
index 2e755bf083b35911ef1295762512091c0b587d84..e9aa17e3345c700c626306adbe7ced8b8388f99c 100644
Binary files a/prediction_service/__pycache__/views.cpython-310.pyc and b/prediction_service/__pycache__/views.cpython-310.pyc differ
diff --git a/prediction_service/templates/prediction_service/model.html b/prediction_service/templates/prediction_service/model.html
index 9303fdad3d3358b8bf0697cbc4a54907a5e71ecf..1fc09a5fbb5ec1962c2b01625e312291179225b8 100644
--- a/prediction_service/templates/prediction_service/model.html
+++ b/prediction_service/templates/prediction_service/model.html
@@ -10,6 +10,14 @@
<img src="data:image/png;base64,{{ img_str }}" alt="PIL Image">
<h2>Model Layers: {{ layer }}</h2>
+
+
+ <form action="/mlmodel/{{ modelID }}/" method="post" enctype="multipart/form-data">
+
+ {% csrf_token %}
+ <input type="file" name="photo" id="photoInput" accept="image/*">
+ <button type="submit">Upload Photo</button>
+ </form>
</div>
diff --git a/prediction_service/views.py b/prediction_service/views.py
index 31cb174b27b3c1fa702f5eb3f332c83868c5f0a2..62349c948b55986e5e88cac19b0e9f81491b1121 100644
--- a/prediction_service/views.py
+++ b/prediction_service/views.py
@@ -1,5 +1,5 @@
from django.shortcuts import render, redirect
-
+from django.http import HttpResponse
from django.contrib import messages
from django.contrib.auth.decorators import login_required
# messages.debug
@@ -119,51 +119,62 @@ import base64
@login_required
def mlmodel(request, pk):
- matrix = np.asarray([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,67,232,39,0,0],
- [0,0,0,0,62,81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,120,180,39,0,0],
- [0,0,0,0,126,163,0,0,0,0,0,0,0,0,0,0,0,0,0,2,153,210,40,0,0],
- [0,0,0,0,220,163,0,0,0,0,0,0,0,0,0,0,0,0,0,27,254,162,0,0,0],
- [0,0,0,0,222,163,0,0,0,0,0,0,0,0,0,0,0,0,0,183,254,125,0,0,0],
- [0,0,0,0,46,245,0,0,0,0,0,0,0,0,0,0,0,0,0,198,254,56,0,0,0],
- [0,0,0,0,120,254,0,0,0,0,0,0,0,0,0,0,0,0,23,231,254,29,0,0,0],
- [0,0,0,0,159,254,0,0,0,0,0,0,0,0,0,0,0,0,163,254,216,16,0,0,0],
- [0,0,0,0,159,254,0,0,0,0,0,0,0,0,0,14,86,178,248,254,91,0,0,0,0],
- [0,0,0,0,159,254,35,0,0,47,49,116,144,150,241,243,234,179,241,252,40,0,0,0,0],
- [0,0,0,0,150,253,237,207,207,207,253,254,250,240,198,143,91,28,233,250,0,0,0,0,0],
- [0,0,0,0,119,177,177,177,177,177,98,56,0,0,0,0,0,102,254,220,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,254,137,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,254,57,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,254,57,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,255,94,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,254,96,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,254,153,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,255,153,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,96,254,153,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
- [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])
-
- # Matrix dimensions
- raw_height = matrix.shape[0]
- raw_width = matrix.shape[1]
-
- fig, ax = plt.subplots(1, 1, figsize=(4, 4))
- ax.imshow(matrix, cmap='Greys_r')
-
- buf = io.BytesIO()
- fig.savefig(buf, format='png')
- buf.seek(0)
- img = Image.open(buf)
-
- # Convert PIL image to base64 string
- buffered = io.BytesIO()
- img.save(buffered, format='PNG')
- img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
-
- mlmodel = MLModel.objects.get(id=pk)
- layercount = str(mlmodel.layers)
-
- return render(request, "prediction_service/model.html", {'img_str': img_str,
- 'layer': layercount,})
\ No newline at end of file
+ if request.method == "POST":
+ # Handle form submission here
+ # For example, you can process the uploaded file
+ uploaded_file = request.FILES['photo']
+ # Process the uploaded file as needed
+
+ # Return a response indicating successful upload
+ return HttpResponse("<h1> You uploaded your photo ! </h1>")
+ else:
+
+ matrix = np.asarray([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,67,232,39,0,0],
+ [0,0,0,0,62,81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,120,180,39,0,0],
+ [0,0,0,0,126,163,0,0,0,0,0,0,0,0,0,0,0,0,0,2,153,210,40,0,0],
+ [0,0,0,0,220,163,0,0,0,0,0,0,0,0,0,0,0,0,0,27,254,162,0,0,0],
+ [0,0,0,0,222,163,0,0,0,0,0,0,0,0,0,0,0,0,0,183,254,125,0,0,0],
+ [0,0,0,0,46,245,0,0,0,0,0,0,0,0,0,0,0,0,0,198,254,56,0,0,0],
+ [0,0,0,0,120,254,0,0,0,0,0,0,0,0,0,0,0,0,23,231,254,29,0,0,0],
+ [0,0,0,0,159,254,0,0,0,0,0,0,0,0,0,0,0,0,163,254,216,16,0,0,0],
+ [0,0,0,0,159,254,0,0,0,0,0,0,0,0,0,14,86,178,248,254,91,0,0,0,0],
+ [0,0,0,0,159,254,35,0,0,47,49,116,144,150,241,243,234,179,241,252,40,0,0,0,0],
+ [0,0,0,0,150,253,237,207,207,207,253,254,250,240,198,143,91,28,233,250,0,0,0,0,0],
+ [0,0,0,0,119,177,177,177,177,177,98,56,0,0,0,0,0,102,254,220,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,254,137,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,254,57,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,254,57,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,255,94,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,254,96,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,254,153,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,169,255,153,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,96,254,153,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])
+
+ # Matrix dimensions
+ raw_height = matrix.shape[0]
+ raw_width = matrix.shape[1]
+
+ fig, ax = plt.subplots(1, 1, figsize=(4, 4))
+ ax.imshow(matrix, cmap='Greys_r')
+
+ buf = io.BytesIO()
+ fig.savefig(buf, format='png')
+ buf.seek(0)
+ img = Image.open(buf)
+
+ # Convert PIL image to base64 string
+ buffered = io.BytesIO()
+ img.save(buffered, format='PNG')
+ img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+ mlmodel = MLModel.objects.get(id=pk)
+ layercount = str(mlmodel.layers)
+
+ return render(request, "prediction_service/model.html", {'img_str': img_str,
+ 'layer': layercount,
+ 'modelID': pk})
\ No newline at end of file