Update app.py

app.py

@@ -1,30 +1,52 @@
 import streamlit as st
-import torch, torch.nn as nn
+import torch
+import torch.nn as nn
 import numpy as np
 from huggingface_hub import hf_hub_download
 
 class IcebergClassifier(nn.Module):
     def __init__(self):
         super().__init__()
-        self.conv = nn.Sequential(nn.Conv2d(2,16,3,padding=1),nn.ReLU(),nn.MaxPool2d(2),
-                                nn.Conv2d(16,32,3,padding=1),nn.ReLU(),nn.MaxPool2d(2),
-                                nn.Conv2d(32,64,3,padding=1),nn.ReLU(),nn.MaxPool2d(2))
-        self.fc = nn.Sequential(nn.Linear(64*9*9,64),nn.ReLU(),nn.Dropout(0.5),
-                              nn.Linear(64,1),nn.Sigmoid())
-    def forward(self, x): return self.fc(self.conv(x).view(x.size(0),-1))
+        self.conv = nn.Sequential(
+            nn.Conv2d(2,16,3), nn.ReLU(), nn.MaxPool2d(2),
+            nn.Conv2d(16,32,3), nn.ReLU(), nn.MaxPool2d(2)
+        )
+        self.fc = nn.Sequential(
+            nn.Linear(32*17*17,64), nn.ReLU(),
+            nn.Linear(64,1), nn.Sigmoid()
+        )
+    def forward(self, x): 
+        return self.fc(self.conv(x).view(x.size(0),-1))
 
 @st.cache_resource
-def predict(b1,b2):
+def load_model():
     model = IcebergClassifier().eval()
-    model.load_state_dict(torch.load(hf_hub_download("alperugurcan/iceberg","best_iceberg_model.pth"),map_location='cpu'))
-    return model(torch.FloatTensor(np.stack([np.array(b1).reshape(75,75),np.array(b2).reshape(75,75)])).unsqueeze(0)).item()
+    model.load_state_dict(torch.load(hf_hub_download("alperugurcan/iceberg","best_iceberg_model.pth"), map_location='cpu'))
+    return model
 
-st.title('🧊 Iceberg vs Ship Classifier')
-examples = {"Ship":[[-32.5]*75]*75,"Iceberg":[[-28.3]*75]*75,"Strong":[[-35.8]*75]*75}
-for k,v in examples.items(): st.code(f"Example ({k}): {v}")
-b1,b2 = st.text_area('Band 1'),st.text_area('Band 2')
-if st.button('Predict'):
+st.title('🧊 Simple Ship vs Iceberg Detector')
+
+# Simple numeric inputs
+band1 = st.number_input('Enter Band 1 value (-40 to -20)', -40.0, -20.0, -30.0)
+band2 = st.number_input('Enter Band 2 value (-35 to -15)', -35.0, -15.0, -25.0)
+
+if st.button('Detect'):
     try:
-        p = predict(eval(b1),eval(b2))
-        st.write(f"{'🧊 ICEBERG' if p>0.5 else '🚢 SHIP'} ({p:.1%})")
-    except: st.error('Invalid input')
+        # Create simple 75x75 arrays with the input values
+        b1 = np.full((75,75), band1)
+        b2 = np.full((75,75), band2)
+        
+        # Prepare input tensor
+        x = torch.FloatTensor(np.stack([b1,b2])).unsqueeze(0)
+        
+        # Get prediction
+        model = load_model()
+        with torch.no_grad():
+            pred = model(x).item()
+        
+        # Show result
+        result = "🧊 ICEBERG" if pred > 0.5 else "🚢 SHIP"
+        st.success(f"{result} ({pred:.1%})")
+        
+    except Exception as e:
+        st.error(f'Error: {str(e)}')