Update app.py

app.py

@@ -1,18 +1,79 @@
 import gradio as gr
+import torch
+import pickle
+import subprocess
+from predictor import Predictor
+from tensorflow.keras.models import load_model
+from ml_simplified_tree import maximum_likelihood
 
+# --------- Load Models & Assets ---------
+# Boundary-aware model
+boundary_model = Predictor("best_boundary_aware_model.pth")
 
+# Keras model
+keras_model = load_model("best_model.keras")
+with open("kmer_to_index.pkl", "rb") as f:
+    kmer_to_index = pickle.load(f)
 
+# Utility function for keras model
+def predict_with_keras(sequence):
+    kmers = [sequence[i:i+6] for i in range(len(sequence)-5)]
+    indices = [kmer_to_index.get(kmer, 0) for kmer in kmers]
+    input_arr = torch.tensor([indices])
+    prediction = keras_model.predict(input_arr)
+    return str(prediction)
 
+# MAFFT + IQTree pipeline
+def run_mafft_and_iqtree():
+    try:
+        subprocess.run(["mafft", "--auto", "f_gene_sequences_aligned.fasta"], check=True)
+        subprocess.run(["iqtree", "-s", "f_gene_sequences.phy.treefile", "-m", "GTR"], check=True)
+        return "MAFFT and IQTree executed successfully."
+    except Exception as e:
+        return f"Error: {str(e)}"
 
+# Maximum Likelihood Tree
+def run_maximum_likelihood():
+    try:
+        result = maximum_likelihood("f gene clean dataset.csv")
+        return result
+    except Exception as e:
+        return f"Error: {str(e)}"
 
+# --------- Gradio Interface ---------
+def boundary_model_predict(sequence):
+    return boundary_model.predict(sequence)
 
-def test_function(text):
-    return f"You entered: {text}"
+gr_interface = gr.TabbedInterface(
+    [
+        gr.Interface(
+            fn=boundary_model_predict,
+            inputs=gr.Textbox(label="DNA Sequence"),
+            outputs=gr.Textbox(label="Boundary Model Prediction"),
+            title="Boundary-Aware Model"
+        ),
+        gr.Interface(
+            fn=predict_with_keras,
+            inputs=gr.Textbox(label="DNA Sequence"),
+            outputs=gr.Textbox(label="Keras Model Prediction"),
+            title="Keras Model"
+        ),
+        gr.Interface(
+            fn=run_mafft_and_iqtree,
+            inputs=[],
+            outputs="text",
+            title="MAFFT + IQTree Runner"
+        ),
+        gr.Interface(
+            fn=run_maximum_likelihood,
+            inputs=[],
+            outputs="text",
+            title="Simplified ML Tree Generator"
+        )
+    ],
+    tab_names=["Boundary Model", "Keras Model", "MAFFT + IQTree", "ML Tree"]
+)
 
-
-# Simple test interface
-with gr.Blocks(title="Test App") as demo:
-    gr.Markdown("# Test Interface")
-    with gr.Row():
-        input_box = gr.Textbox(label="Input")
-        output_box = gr.Textbox(label="Output")
+# --------- Launch ---------
+if __name__ == "__main__":
+    gr_interface.launch()