Update models.py

models.py

@@ -1,11 +1,12 @@
 # ==============================
-# MODEL + FUSION LOGIC
+# MODEL + MODALITY PROCESSING
 # ==============================
 
 from transformers import pipeline
 import torch
 import time
 from datetime import datetime
+from fusion import compute_fusion
 
 device = 0 if torch.cuda.is_available() else -1
 
@@ -126,58 +127,13 @@ def multimodal_analyze(text, image, audio):
 """
         except Exception as e:
             audio_result_display = f"Audio processing error: {str(e)}"
-            audio_label = None
-            audio_conf = 0
 
     # -------- FUSION --------
-    reasoning_lines = []
-    fusion_score = 0
-    total_weight = 0
-
-    if text_label:
-        reasoning_lines.append(
-            f"Text suggests a {text_label.lower()} tone ({text_conf}%)."
-        )
-        weight = 0.4
-        fusion_score += text_conf * weight if text_label == "POSITIVE" else -text_conf * weight
-        total_weight += weight
-
-    if audio_label:
-        reasoning_lines.append(
-            f"Spoken content carries a {audio_label.lower()} tone ({audio_conf}%)."
-        )
-        weight = 0.35
-        fusion_score += audio_conf * weight if audio_label == "POSITIVE" else -audio_conf * weight
-        total_weight += weight
-
-    if image_label and image_conf > 40:
-        reasoning_lines.append(
-            f"Image classified as {image_label} ({image_conf}%)."
-        )
-        weight = 0.25
-        fusion_score += image_conf * weight
-        total_weight += weight
-
-    if total_weight > 0:
-        fusion_score = fusion_score / total_weight
-
-    fusion_score = max(min(fusion_score, 100), -100)
-
-    if fusion_score > 60:
-        alignment_message = "Strong positive multimodal alignment detected."
-        color = "#22c55e"
-    elif fusion_score > 20:
-        alignment_message = "Moderate positive contextual signals observed."
-        color = "#facc15"
-    elif fusion_score > -20:
-        alignment_message = "Mixed or neutral signals across modalities."
-        color = "#94a3b8"
-    elif fusion_score > -60:
-        alignment_message = "Moderate negative contextual alignment detected."
-        color = "#f97316"
-    else:
-        alignment_message = "Strong negative multimodal alignment detected."
-        color = "#ef4444"
+    fusion_score, reasoning_lines, alignment_message, color = compute_fusion(
+        text_label, text_conf,
+        image_label, image_conf,
+        audio_label, audio_conf
+    )
 
     processing_time = round(time.time() - start_time, 2)