emotions fix

app.py

@@ -31,7 +31,7 @@ apikey = os.environ["GEMINI_API_KEY"]  # Replace or use os.getenv if secret
 
 # Configure Gemini API for drafting (free)
 genai_ext.configure(api_key=apikey)
-llm_model = genai_ext.GenerativeModel('gemini-1.5-flash')
+llm_model = genai_ext.GenerativeModel('gemini-1.5-pro')
 
 # Real classifiers
 emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")  # For D
@@ -105,7 +105,7 @@ class HumanLikeChatbot:
             user_emotion = emotion_result['label']
 
             # Update bot's mood and irritation
-            if user_emotion in ['anger', 'disgust'] or any(word in clean_message for word in ['bakwaas', 'stupid', 'idiot']):
+            if user_emotion in ['anger', 'disgust'] or any(word in clean_message for word in [ 'stupid', 'idiot']):
                 self.irritation_level += 0.2  # Build irritation
                 if self.irritation_level > 0.5:
                     self.bot_mood = "irritated"
@@ -115,15 +115,16 @@ class HumanLikeChatbot:
             elif user_emotion in ['sadness', 'disappointment']:
                 self.bot_mood = "emotional"
                 I = 0.7
-                self.irritation_level = max(0, self.irritation_level - 0.1)  # Reduce irritation
+                self.irritation_level = max(0, self.irritation_level - 0.05 if user_emotion == 'sadness' else self.irritation_level - 0.1)  # Slow decrease for sadness, medium for disappointment
             elif user_emotion == 'joy':
                 self.bot_mood = "happy"
                 I = 0.9
-                self.irritation_level = 0  # Reset irritation
+                self.irritation_level = max(0, self.irritation_level - 0.3)  # Larger decrease for joy, but not instant reset
             else:
                 self.bot_mood = "neutral"
                 I = 0.5
-                self.irritation_level = max(0, self.irritation_level - 0.1)
+                self.irritation_level = max(0, self.irritation_level - 0.05)  # Slow decrease in neutral
+
 
             # Draft response from LLM based on bot's mood
             prompt = f"""User said: "{clean_message}" | User Mood: {user_emotion} | Bot Mood: {self.bot_mood} | History: {self.history[-2:]} → Reply as a  English chatbot , based on this {self.bot_mood}, human-like, no tips or instructions JUST LIKE YOU ARE A HUMAN WITH FEELINGS AND SENSES:"""
@@ -162,7 +163,7 @@ class HumanLikeChatbot:
             self.history.append(clean_message)
             return full_resp + f" (E Score: {score:.2f})"
         except Exception as e:
-            return f"Error aaya bhai: {str(e)}. Endpoint ya auth check kar."
+            return f"Error : {str(e)}."
 
 # Gradio app
 def chat(message, history):
@@ -177,7 +178,7 @@ bot = HumanLikeChatbot()
 with gr.Blocks(title="HumanLike Chatbot") as demo:
     gr.Markdown("<h1 style='text-align: center;'>HumanLike Chatbot with Emotions and E Score</h1>")
     chatbot = gr.Chatbot(height=400)
-    msg = gr.Textbox(label="Tu:", placeholder="Type your message here...")
+    msg = gr.Textbox(label="You:", placeholder="Type your message here...")
     clear = gr.Button("Clear")
 
     msg.submit(chat, [msg, chatbot], [msg, chatbot])