Update app.py

app.py

@@ -108,10 +108,11 @@ def run_deep_learning_cascade(dna_sequence):
         is_coding = False
         confidence = (1 - p_coding) if p_coding < 0.50 else 0.85
 
-    # --- LEVEL 3: The Deep Learning Branching Logic ---
-    final_label = f"{'GENE' if is_coding else 'NON-CODING'}\tFeature: {structural_feature}"
-
-    # BRANCH A: Phenotype Analysis (Triggered if Coding AND is CDS/Exon)
+# --- LEVEL 3: The Deep Learning Branching Logic ---
+    summary_dict = {
+        "Final Classification": "GENE" if is_coding else "NON-CODING",
+        "Feature": structural_feature
+    }
 
     # BRANCH A: Phenotype Analysis (Triggered if Coding AND is CDS/Exon)
     if is_coding and lgb_prediction == 0:
@@ -125,7 +126,7 @@ def run_deep_learning_cascade(dna_sequence):
             prob_obese, prob_lean = probs[0][0].item(), probs[0][1].item()
             
             phenotype = "Obesity-Associated" if prob_obese > prob_lean else "Lean-Associated"
-            final_label += f" | Phenotype: {phenotype}"
+            summary_dict["Phenotype"] = phenotype
             raw_scores["Phenotype (Obese)"] = prob_obese
             raw_scores["Phenotype (Lean)"] = prob_lean
 
@@ -139,10 +140,10 @@ def run_deep_learning_cascade(dna_sequence):
             p_promoter = probs[0][0].item()
             
             validation = "High Confidence Regulatory Element" if p_promoter >= 0.50 else "Weak Regulatory Signal"
-            final_label += f"\nValidation: {validation}"
-            raw_scores["Promoter Signal"] = p_promoter
+            summary_dict["Validation"] = validation
+            raw_scores["DNABERT-2 Promoter Signal"] = p_promoter
 
-    return final_label, confidence, raw_scores
+    return summary_dict, confidence, raw_scores
 
 # ===================================
 # 3. SPATIAL MAPPING (NCBI / ENSEMBL)
@@ -239,7 +240,7 @@ def gradio_inference(dna_sequence, run_mapping):
     summary_html = f"""
     <div style="border: 1px solid #4b5563; border-radius: 8px; padding: 15px; margin-bottom: 15px;">
         <h3 style="margin-top: 0; margin-bottom: 10px;"> Classification Summary <span style="color: #10b981; font-size: 0.85em; font-weight: normal;">(Deep Scan Complete)</span></h3>
-        <div style="font-size: 1.15em; line-height: 1.8; placeholder: 'Results will appear here...'">
+        <div style="font-size: 1.15em; line-height: 1.8">
             Final Classification: <b style="color: #0d6efd;">{summary_dict.get('Final Classification')}</b><br>
             Feature: <b>{summary_dict.get('Feature')}</b><br>
     """