Update app.py

app.py

@@ -2,18 +2,6 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 from transformers import pipeline
 
-# Initialize the emotion classifier
-classifier = pipeline("text-classification", model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)
-
-# Define the function for emotion detection
-def detect_emotions(emotion_input):
-    prediction = classifier(emotion_input)
-    output = {emotion["label"]: emotion["score"] for emotion in prediction[0]}
-    return output
-
-# Examples for the emotion detector
-examples = [["I am happy that I gifted my son a robot"], ["Sorry for being late"]]
-
 # CSS to hide footer and customize button
 css = """
 footer {display:none !important}
@@ -144,6 +132,18 @@ Hello! I'm here to support you emotionally and answer any questions. How are you
 <div style='color: green;'>Developed by Hashir Ehtisham</div>
 """
 
+# Motivational tagline for the new tab
+motivational_tagline = """
+Welcome to the Motivational Quotes tab! Let’s ignite your day with some inspiration. What do you need motivation for today?
+<div style='color: green;'>Developed by Hashir Ehtisham</div>
+"""
+
+# Emotions Detector tagline for the new tab
+emotions_detector_tagline = """
+Know how your message sounds and how to improve the tone of the message with Emotions Detector.
+<div style='color: green;'>Developed by Hashir Ehtisham</div>
+"""
+
 # Define the Gradio Blocks interface
 with gr.Blocks(css=css) as demo:
     with gr.Tab("Emotional Support Chatbot"):
@@ -174,14 +174,61 @@ with gr.Blocks(css=css) as demo:
         msg.submit(respond_wrapper, [msg, chatbot, system_message, max_tokens, temperature, top_p], [msg, chatbot])
         clear.click(lambda: None, None, chatbot, queue=False)
     
+    with gr.Tab("Motivational Quotes"):
+        gr.Markdown("# Motivational Quotes")
+        gr.Markdown(motivational_tagline)
+        
+        system_message_motivational = gr.Textbox(value="You are a friendly Motivational Quotes Chatbot.", visible=False)
+        chatbot_motivational = gr.Chatbot()
+        msg_motivational = gr.Textbox(label="Your message")
+        clear_motivational = gr.Button("Clear")
+        
+        with gr.Accordion("Additional Inputs", open=False):
+            max_tokens_motivational = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+            temperature_motivational = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+            top_p_motivational = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+        def respond_wrapper_motivational(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
+            chat_history, _ = send_message(
+                message=message,
+                history=chat_history,
+                system_message=system_message_val,
+                max_tokens=max_tokens_val,
+                temperature=temperature_val,
+                top_p=top_p_val,
+            )
+            return gr.update(value=""), chat_history
+
+        msg_motivational.submit(respond_wrapper_motivational, [msg_motivational, chatbot_motivational, system_message_motivational, max_tokens_motivational, temperature_motivational, top_p_motivational], [msg_motivational, chatbot_motivational])
+        clear_motivational.click(lambda: None, None, chatbot_motivational, queue=False)
+    
     with gr.Tab("Emotions Detector"):
-        gr.Interface(
-            fn=detect_emotions, 
-            inputs=gr.Textbox(placeholder="Enter text here", label="Input"), 
-            outputs=gr.Label(label="Emotion"), 
-            title="Emotion Detector ", 
-            examples=examples
-        )
+        gr.Markdown("# Emotions Detector")
+        gr.Markdown(emotions_detector_tagline)
+        
+        system_message_emotions = gr.Textbox(value="You are an Emotions Detector Chatbot. Analyze the tone of the message (happy, sad, funny, excited, regret, depressed, joy, surprise, fear, anger) and provide suggestions to improve the message.", visible=False)
+        chatbot_emotions = gr.Chatbot()
+        msg_emotions = gr.Textbox(label="Your message")
+        clear_emotions = gr.Button("Clear")
+        
+        with gr.Accordion("Additional Inputs", open=False):
+            max_tokens_emotions = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+            temperature_emotions = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+            top_p_emotions = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+        def respond_wrapper_emotions(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
+            chat_history, _ = send_message(
+                message=message,
+                history=chat_history,
+                system_message=system_message_val,
+                max_tokens=max_tokens_val,
+                temperature=temperature_val,
+                top_p=top_p_val,
+            )
+            return gr.update(value=""), chat_history
+
+        msg_emotions.submit(respond_wrapper_emotions, [msg_emotions, chatbot_emotions, system_message_emotions, max_tokens_emotions, temperature_emotions, top_p_emotions], [msg_emotions, chatbot_emotions])
+        clear_emotions.click(lambda: None, None, chatbot_emotions, queue=False)
 
 if __name__ == "__main__":
     demo.launch()