Update app.py

app.py

@@ -1,38 +1,53 @@
 import gradio as gr
 
-from baseline.baseline_convnext import predict_convnext
-from baseline.baseline_infer import predict_baseline
+# --- 1. Import Existing Baselines ---
+# Wrapped in try-except so the app doesn't crash if files are temporarily missing
+try:
+    from baseline.baseline_convnext import predict_convnext
+except ImportError:
+    def predict_convnext(image): return {"Error": "ConvNeXt module missing"}
+
+try:
+    from baseline.baseline_infer import predict_baseline
+except ImportError:
+    def predict_baseline(image): return {"Error": "Baseline module missing"}
+
+# --- 2. Import NEW SPA Approach ---
+# This imports the function from: new_approach/spa_ensemble.py
+try:
+    from new_approach.spa_ensemble import predict_spa
+except ImportError:
+    def predict_spa(image): return {"Error": "SPA module missing. Check 'new_approach' folder."}
 
 
 # --- Placeholder models (for future extensions) ---
-def predict_placeholder_1(image):
-    if image is None:
-        return "Please upload an image."
-    return "Model 2 is not available yet. Please check back later."
-
-
 def predict_placeholder_2(image):
     if image is None:
         return "Please upload an image."
-    return "Model 3 is not available yet. Please check back later."
-
+    return "Model 4 is not available yet. Please check back later."
 
 # --- Main Prediction Logic ---
 def predict(model_choice, image):
-    if model_choice == "Herbarium Species Classifier":
-        # Friend's ConvNeXt mix-stream CNN baseline
+    if image is None: return None
+
+    if model_choice == "Herbarium Species Classifier (ConvNeXT)":
+        # Luna's ConvNeXt mix-stream CNN baseline
         return predict_convnext(image)
+        
     elif model_choice == "Baseline (DINOv2 + LogReg)":
-        # Your plant-pretrained DINOv2 + Logistic Regression baseline
+        # Islam's baseline
         return predict_baseline(image)
-    elif model_choice == "Future Model 1 (Placeholder)":
-        return predict_placeholder_1(image)
+        
+    elif model_choice == "SPA Ensemble (New Approach)":
+        # New approach: DINOv2 + BioCLIP + Handcrafted + SPA
+        return predict_spa(image)
+        
     elif model_choice == "Future Model 2 (Placeholder)":
         return predict_placeholder_2(image)
+        
     else:
         return "Invalid model selected."
 
-
 # --- Gradio Interface ---
 with gr.Blocks(theme=gr.themes.Soft(), css="style.css") as demo:
     with gr.Column(elem_id="app-wrapper"):
@@ -41,24 +56,24 @@ with gr.Blocks(theme=gr.themes.Soft(), css="style.css") as demo:
             """
             <div id="app-header">
               <h1>🌿 Plant Species Classification</h1>
-              <h3>AML Group Project – PsychicFireSong</h3>
+              <h3>AML Group Project – Group 8</h3>
             </div>
             """,
             elem_id="app-header",
         )
-
+        
         # Badges row
         gr.Markdown(
             """
             <div id="badge-row">
-              <span class="badge">Herbarium + Field images</span>
-              <span class="badge">ConvNeXtV2 mix-stream CNN</span>
-              <span class="badge">DINOv2 + Logistic Regression</span>
+              <span class="badge">Herbarium + Field images (ConvNeXT)</span>
+              <span class="badge">ConvNeXtV2</span>
+              <span class="badge">SPA Ensemble</span>
             </div>
             """,
             elem_id="badge-row",
         )
-
+        
         # Main card
         with gr.Row(elem_id="main-card"):
             # Left side: model + image
@@ -68,26 +83,28 @@ with gr.Blocks(theme=gr.themes.Soft(), css="style.css") as demo:
                     choices=[
                         "Herbarium Species Classifier",
                         "Baseline (DINOv2 + LogReg)",
-                        "Future Model 1 (Placeholder)",
+                        "SPA Ensemble (New Approach)",
                         "Future Model 2 (Placeholder)",
                     ],
-                    value="Herbarium Species Classifier",
+                    value="SPA Ensemble (New Approach)", # Default to your new model
                 )
-
+                
                 gr.Markdown(
                     """
                     <div id="model-help">
-                      <b>Herbarium Species Classifier</b> – end-to-end ConvNeXtV2 CNN.<br>
-                      <b>Baseline</b> – plant-pretrained DINOv2 features + logistic regression head.
+                      <b>Herbarium Classifier</b> – ConvNeXtV2 CNN.<br>
+                      <b>Baseline</b> – Simple DINOv2 + LogReg.<br>
+                      <b>SPA Ensemble</b> – <i>(New)</i> DINOv2 + BioCLIP-2 + Handcrafted features.
                     </div>
                     """,
                     elem_id="model-help",
                 )
-
+                
                 image_input = gr.Image(
                     type="pil",
                     label="Upload plant image",
                 )
+                
                 submit_button = gr.Button("Classify ���", variant="primary")
 
             # Right side: predictions
@@ -103,19 +120,19 @@ with gr.Blocks(theme=gr.themes.Soft(), css="style.css") as demo:
             outputs=output_label,
         )
 
-        # Optional examples (keep empty if you don't have images)
+        # Optional examples
         gr.Examples(
             examples=[],
             inputs=image_input,
             outputs=output_label,
-            fn=lambda img: predict("Herbarium Species Classifier", img),
+            fn=lambda img: predict("SPA Ensemble (New Approach)", img),
             cache_examples=False,
         )
 
         gr.Markdown(
-            "Built for the AML course – compare CNN vs. DINOv2 feature-extractor baselines.",
+            "Built for the AML course – compare CNN vs. DINOv2 feature-extractor baselines with the new approaches to address cross-domain plant identification.",
             elem_id="footer",
         )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()