Update model.py

model.py

@@ -77,21 +77,15 @@ Prediction: **{text_label}**
 
 Confidence: **{text_conf}%**
 """
-
         except Exception as e:
             text_result_display = f"Text error: {str(e)}"
 
     # ================= IMAGE =================
-    image_path = None
     if image is not None:
         try:
-            
-            # create image folder if not exists
-            
             results = image_pipeline(image)
 
             image_result_display = "## 🖼 Image Classification\n\n"
-            
 
             for r in results[:3]:
                 label = r["label"]
@@ -101,10 +95,6 @@ Confidence: **{text_conf}%**
             image_label = results[0]["label"]
             image_conf = round(results[0]["score"] * 100, 2)
 
-            # image preview
-            
-
-            
         except Exception as e:
             image_result_display = f"Image error: {str(e)}"
 
@@ -139,37 +129,103 @@ Confidence: **{audio_conf}%**
         except Exception as e:
             audio_result_display = f"Audio error: {str(e)}"
 
-    # ================= FUSION =================
-    fusion_score, reasoning, interpretation, color = compute_fusion(
-        text_label, text_conf,
-        image_label, image_conf,
-        audio_label, audio_conf
-    )
+    # ================= FUSION REASONING =================
+
+    reasoning_lines = []
+
+    if text_label:
+        reasoning_lines.append(
+            f"The textual input expresses a {text_label.lower()} emotional tone ({text_conf}% confidence)."
+        )
+
+    if image_label:
+        reasoning_lines.append(
+            f"The uploaded image is most likely '{image_label}' ({image_conf}% confidence)."
+        )
+
+    if transcription:
+        reasoning_lines.append(
+            "The audio transcription indicates spoken conversational content."
+        )
+
+    # ================= FUSION SCORE =================
+
+    fusion_score = 0
+
+    if text_label == "POSITIVE":
+        fusion_score += text_conf * 0.5
+    elif text_label == "NEGATIVE":
+        fusion_score -= text_conf * 0.5
+
+    if image_label:
+        fusion_score += image_conf * 0.3
+
+    if transcription:
+        fusion_score += 20
+
+    # ================= INTERPRETATION =================
+
+    if fusion_score > 60:
+        alignment_message = "Multimodal signals align toward a positive and confident contextual interpretation."
+        color = "#22c55e"
+
+    elif fusion_score < 0:
+        alignment_message = "Multimodal signals indicate a potentially negative contextual alignment."
+        color = "#ef4444"
+
+    else:
+        alignment_message = "Multimodal signals are contextually mixed or neutral."
+        color = "#f59e0b"
 
     processing_time = round(time.time() - start_time, 2)
 
+    # ================= DISPLAY SUMMARY =================
+
     fusion_summary = f"""
+<div style="padding:20px;border-radius:16px;
+background:linear-gradient(135deg,#0f172a,#1e293b);
+border:1px solid #1f2a44;">
+
 <h2>🔎 Multimodal Intelligence Summary</h2>
 
-{"<br>".join(reasoning)}
+{"<br>".join(reasoning_lines)}
 
 <hr>
 
-<h3>Fusion Score</h3>
-<span style="color:{color}; font-size:24px; font-weight:bold;">
+<h3>📊 Fusion Score</h3>
+
+<span style="color:{color};
+font-size:32px;
+font-weight:700;">
 {round(fusion_score,2)}
 </span>
+
 <hr>
 
-<h3>Interpretation</h3>
+<h3>🧠 Interpretation</h3>
 
-{interpretation}
+{alignment_message}
 
 <br>
 
 ⏱ Processing Time: {processing_time} sec
+
+</div>
 """
 
+    # ================= SAVE HISTORY =================
+
+    save_analysis({
+        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "text": text,
+        "image": image_label,
+        "audio": audio_label,
+        "transcription": transcription,
+        "fusion_score": round(fusion_score, 2)
+    })
+
+    return fusion_summary, text_result_display, image_result_display, audio_result_display
+
     # ================= SAVE HISTORY =================
     save_analysis({
         "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),