Update model.py

model.py

@@ -338,11 +338,11 @@ def generate_response(emotions, tone="neutral", context=None):
                 "It’s wonderful to feel love! Want to share more?"
             ],
             "nervousness": [
-                "It’s okay to feel nervous. What’s making you anxious? 😬",
-                "I understand that feeling. Is there anything I can do to help?",
-                "Sometimes nerves mean something important is coming up! What’s happening?",
-                "It’s completely natural to feel this way. Want to talk about it?",
-                "I hear you. Is there anything that might help ease your nerves?"
+                "It’s completely normal to feel this way. You’ve got this! 💪",
+                "I totally understand. Do you want some tips for staying calm?",
+                "Nerves can mean you care about doing well! What’s on your mind?",
+                "You’re going to do great! Want to talk through any concerns?",
+                "Feeling nervous shows this is important to you. How can I support you?"
             ],
             "optimism": [
                 "That’s great! Optimism can change everything. What’s giving you hope today? 🌞",
@@ -425,10 +425,19 @@ def generate_response(emotions, tone="neutral", context=None):
 
 # Smooth Emotion Transitions
 def smooth_emotion_transition(prev_emotions, current_emotions):
+    if not prev_emotions:
+        return current_emotions
+
     if "joy" in prev_emotions and "sadness" in current_emotions:
         return "That’s quite a mix of emotions. Let’s talk about it! "
     elif "anger" in prev_emotions and "relief" in current_emotions:
-        return "It sounds like you’ve been through a lot. "
+        return "It sounds like you’ve been through a lot, but I’m glad you’re feeling some relief. "
+    elif "fear" in prev_emotions and "excitement" in current_emotions:
+        return "It’s completely normal to feel both excited and a little anxious. That means it’s important to you! "
+    elif "confusion" in prev_emotions and "realization" in current_emotions:
+        return "It sounds like things are becoming clearer for you. That’s great! "
+    elif "nervousness" in prev_emotions and "pride" in current_emotions:
+        return "You might have started off nervous, but it sounds like you’re feeling more confident now. "
     else:
         return ""
 
@@ -442,10 +451,10 @@ def process_multiple_sentences(text, context):
         emotions = predict_emotions(sentence)
         all_emotions.extend(emotions)
 
-        adjusted_emotions = smooth_emotion_transition(context.previous_emotions, emotions)
-        context.update(sentence, adjusted_emotions, "")
+        transition_text = smooth_emotion_transition(context.previous_emotions, emotions)
+        context.update(sentence, emotions, transition_text)
 
-        response = generate_response(adjusted_emotions, context=context)
+        response = generate_response(emotions, context=context)
         responses.append(response)
 
     return merge_responses(responses, all_emotions)
@@ -455,25 +464,51 @@ def merge_responses(responses, emotions):
     if len(responses) == 1:
         return responses[0]
 
+    # Define emotion categories
+    positive_emotions = {"joy", "admiration", "optimism", "pride", "relief", "love", "gratitude", "excitement", "approval", "curiosity", "desire", "realization"}
+    negative_emotions = {"sadness", "anger", "fear", "disappointment", "remorse", "grief", "disgust", "disapproval", "annoyance"}
+    neutral_emotions = {"neutral", "confusion", "nervousness", "surprise", "caring"}
+
+    # Find the dominant emotion (most frequently occurring)
     dominant_emotion = max(set(emotions), key=emotions.count)
 
-    if "sadness" in emotions and "joy" in emotions:
-        transition = "That’s quite a mix of emotions. Let’s talk about it! "
+    # Transition phrases based on emotion types
+    if any(e in positive_emotions for e in emotions) and any(e in negative_emotions for e in emotions):
+        transition = "I can see you’re experiencing a mix of emotions. Let’s explore this together. "
     elif "anger" in emotions and "relief" in emotions:
-        transition = "It sounds like you’ve been through a lot. "
+        transition = "It sounds like you’ve been through a lot, but I’m glad there’s some relief. "
+    elif "nervousness" in emotions and "excitement" in emotions:
+        transition = "It sounds like a mix of nerves and excitement—totally normal! You must really care about this. "
+    elif "fear" in emotions and "excitement" in emotions:
+        transition = "It’s completely natural to feel both excited and a little scared. That just means it matters to you! "
+    elif "disappointment" in emotions and "approval" in emotions:
+        transition = "I hear that there were some ups and downs in this experience. Let’s talk about both sides. "
+    elif "confusion" in emotions and "realization" in emotions:
+        transition = "It sounds like you were uncertain at first, but now things are becoming clearer. "
     else:
         transition = ""
 
-    return transition + " ".join(responses)
+    # Merge responses with varied connectors
+    if len(responses) == 2:
+        return transition + " ".join(responses)
+    else:
+        # Use varied sentence connectors to avoid repetition
+        connectors = ["On another note,", "At the same time,", "Also,", "One more thing,", "Additionally,"]
+        merged_responses = [responses[0]] + [f"{random.choice(connectors)} {r}" for r in responses[1:]]
+        return transition + " ".join(merged_responses)
 
-# Chatbot Response Function (without greetings, farewells, and fallback)
+# Chatbot Response Function 
 def chatbot_response(text, context):
     try:
         if not text.strip():
-            return ""
+            return "I'm here for you. Feel free to share your thoughts."
 
         emotions = predict_emotions(text)
-        tone = "neutral"  # Placeholder for tone detection
+        tone = analyze_tone(text)
+
+        # Process multi-sentence input
+        if ". " in text:
+            return process_multiple_sentences(text, context)
 
         # Generate chatbot response
         response = generate_response(emotions, tone, context)