update

jagadeshchilla · Oct 17, 2024 · cebf452 · cebf452
1 parent 21f5988
commit cebf452
Showing 1 changed file with 85 additions and 85 deletions.
diff --git a/pages/Image_prediction.py b/pages/Image_prediction.py
@@ -13,92 +13,92 @@
 import gdown
 import tempfile
 
-def show_image_prediction():
-    # Initialize session state attributes
-    if 'saved_predictions' not in st.session_state:
-        st.session_state.saved_predictions = []
-    if 'predictions' not in st.session_state:
-        st.session_state.predictions = []
-    if 'uploaded_images' not in st.session_state:
-        st.session_state.uploaded_images = []
-    if 'model_temp_file' not in st.session_state:
-        st.session_state.model_temp_file = None
-
-    # Define the model links
-    model_links = {
-        'CNN': {
-            'url': 'https://drive.google.com/uc?id=1mtDtPtM-E7y20LlFlEPn1UI20fykFL2P',
-            'target_size': (128, 128)
-        },
-        'ResNet50': {
-            'url': 'https://drive.google.com/uc?id=1F47j2nr5JSa09mBWtUsWOkQYl5pLixqG',
-            'target_size': (260, 260)
-        },
-        'EfficientNet': {
-            'url': 'https://drive.google.com/uc?id=10mXZQWQ1RyGx6BqEsaJdcv8NcSKv-v8A',
-            'target_size': (260, 260)
-        },
-        'DenseNet': {
-            'url': 'https://drive.google.com/uc?id=14-7XGitYTJTYAksSI-LPS-b_lW7Dn8aC',
-            'target_size': (224, 224)
-        },
-        'VGG19': {
-            'url': 'https://drive.google.com/uc?id=19o-JaeGBDXpITObAVkII2sNFYp3qmGoH',
-            'target_size': (224, 224)
-        },
-    }
-
-    def load_existing_predictions():
-        if os.path.exists('prediction_history.json'):
-            with open('prediction_history.json', 'r') as f:
-                return json.load(f)
-        return []
-
-    # Load existing predictions into session state
-    st.session_state.saved_predictions = load_existing_predictions()
-
-    def save_predictions_to_history(uploaded_files, predictions, model_name):
-        prediction_data = []
-        for i, uploaded_file in enumerate(uploaded_files):
-            actual = 'Cancer' if predictions[i][0] == 0 else 'Non Cancer'
-            prediction_data.append({
-                'file_name': uploaded_file.name,
-                'model_used': model_name,
-                'prediction': actual
-            })
-
-        st.session_state.saved_predictions.extend(prediction_data)
-
-        with open('prediction_history.json', 'w') as f:
-            json.dump(st.session_state.saved_predictions, f, indent=4)
-        st.success("Predictions saved to history successfully.")
-
-    cancer_warning_messages = [
-        "Please consult a doctor immediately.",
-        "We recommend scheduling a medical check-up soon.",
-        "It's crucial to seek medical advice right away.",
-        "Contact your healthcare provider for further examination.",
-        "This result may be concerning. Please consult a specialist."
-    ]
-
-    def download_and_load_model(model_url):
-        """Downloads and loads the model from the provided Google Drive URL."""
-        if st.session_state.model_temp_file is None:
-            with tempfile.NamedTemporaryFile(suffix='.keras', delete=False) as tmp:
-                st.session_state.model_temp_file = tmp.name
-                # Show toast message for downloading model
-                st.toast("📥 Downloading model... Please wait.")
-                # Spinner for downloading the model
-                with st.spinner("Downloading the model..."):
-                    gdown.download(model_url, st.session_state.model_temp_file, quiet=False)
-
-                # Show toast message for download completion
-                st.toast("✅ Model download completed!")
-
-        # Load the model from the temp file
-        model = load_model(st.session_state.model_temp_file)
-        return model
+# Define the model links
+model_links = {
+    'CNN': {
+        'url': 'https://drive.google.com/uc?id=1mtDtPtM-E7y20LlFlEPn1UI20fykFL2P',
+        'target_size': (128, 128)
+    },
+    'ResNet50': {
+        'url': 'https://drive.google.com/uc?id=1F47j2nr5JSa09mBWtUsWOkQYl5pLixqG',
+        'target_size': (260, 260)
+    },
+    'EfficientNet': {
+        'url': 'https://drive.google.com/uc?id=10mXZQWQ1RyGx6BqEsaJdcv8NcSKv-v8A',
+        'target_size': (260, 260)
+    },
+    'DenseNet': {
+        'url': 'https://drive.google.com/uc?id=14-7XGitYTJTYAksSI-LPS-b_lW7Dn8aC',
+        'target_size': (224, 224)
+    },
+    'VGG19': {
+        'url': 'https://drive.google.com/uc?id=19o-JaeGBDXpITObAVkII2sNFYp3qmGoH',
+        'target_size': (224, 224)
+    },
+}
+
+# Initialize session state
+if 'saved_predictions' not in st.session_state:
+    st.session_state.saved_predictions = []
+if 'predictions' not in st.session_state:
+    st.session_state.predictions = []
+if 'uploaded_images' not in st.session_state:
+    st.session_state.uploaded_images = []
+if 'model_temp_file' not in st.session_state:
+    st.session_state.model_temp_file = None
+
+def load_existing_predictions():
+    if os.path.exists('prediction_history.json'):
+        with open('prediction_history.json', 'r') as f:
+            return json.load(f)
+    return []
+
+# Load existing predictions into session state
+st.session_state.saved_predictions = load_existing_predictions()
+
+def save_predictions_to_history(uploaded_files, predictions, model_name):
+    prediction_data = []
+    for i, uploaded_file in enumerate(uploaded_files):
+        actual = 'Cancer' if predictions[i][0] == 0 else 'Non Cancer'
+        prediction_data.append({
+            'file_name': uploaded_file.name,
+            'model_used': model_name,
+            'prediction': actual
+        })
+
+    st.session_state.saved_predictions.extend(prediction_data)
+
+    with open('prediction_history.json', 'w') as f:
+        json.dump(st.session_state.saved_predictions, f, indent=4)
+    st.success("Predictions saved to history successfully.")
+
+cancer_warning_messages = [
+    "Please consult a doctor immediately.",
+    "We recommend scheduling a medical check-up soon.",
+    "It's crucial to seek medical advice right away.",
+    "Contact your healthcare provider for further examination.",
+    "This result may be concerning. Please consult a specialist."
+]
+
+def download_and_load_model(model_url):
+    """Downloads and loads the model from the provided Google Drive URL."""
+    if st.session_state.model_temp_file is None:
+        with tempfile.NamedTemporaryFile(suffix='.keras', delete=False) as tmp:
+            st.session_state.model_temp_file = tmp.name
+            # Show toast message for downloading model
+            st.toast("📥 Downloading model... Please wait.")
+            # Spinner for downloading the model
+            with st.spinner("Downloading the model..."):
+                gdown.download(model_url, st.session_state.model_temp_file, quiet=False)
+
+            # Show toast message for download completion
+            st.toast("✅ Model download completed!")
+
+    # Load the model from the temp file
+    model = load_model(st.session_state.model_temp_file)
+    return model
 
+def show_image_prediction():
     # Streamlit UI
     st.title('Oral Cancer Detection Model Evaluation')