v0.1

app.py

@@ -27,9 +27,6 @@ models = {
 }
 
 def predict_sentiment(message, model_name="MultinomialNB"):
-    if not message.strip():
-        return None
-    
     model, vectorizer = models[model_name]
     preprocessed = preprocess_text(message)
     vectorized = vectorizer.transform([preprocessed])
@@ -39,40 +36,46 @@ def predict_sentiment(message, model_name="MultinomialNB"):
 def get_bot_response(message, chat_history, model_choice):
     message = message["text"]
     if not message.strip():
+        bot_response = "😺 Please share a game review!"
+        chat_history.append({"role": "user", "content": message})
+        chat_history.append({"role": "assistant", "content": bot_response})
         return "", chat_history
     
     # Get sentiment prediction
     sentiment = predict_sentiment(message, model_choice)
     
-    if sentiment is None:
-        bot_response = "Please share your thoughts about a game!"
-        chat_history.append((message, bot_response))
-        return "", chat_history
-    
     # Generate response based on sentiment
     if sentiment == 1:
-        responses = [
-            f"This is a Positive review 😃"
-        ]
-        bot_response = responses[len(chat_history) % len(responses)]
+        bot_response = f"😸 This is a Positive review!"
     else:
-        responses = [
-            f"This is a Negative review 🤬"
-        ]
-        bot_response = responses[len(chat_history) % len(responses)]
-    
-    chat_history.append((message, bot_response))
+        bot_response = f"😾 This is a Negative review!"
+   
+    chat_history.append({"role": "user", "content": message})
+    chat_history.append({"role": "assistant", "content": bot_response})
     return "", chat_history
 
 # Create the Gradio interface
 with gr.Blocks(theme=gr.themes.Default(), title="Gaming Sentiment Chatbot", css=".upload-button {display: none;} .centered-md {text-align: center}") as demo:
     gr.Markdown("# 🎮 Steam Review Sentiment Analysis", elem_classes="centered-md")
-    gr.Markdown("✨ Enter a Steam review to analyze its sentiment. For more information, see the dataset used at [Kaggle Steam Reviews](https://www.kaggle.com/datasets/filipkin/steam-reviews)", elem_classes="centered-md")
-    gr.Markdown("[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/alyzbane/gradio-sentimental-analysis-ml)") 
+    gr.Markdown("""
+    <div style="display: flex; justify-content: center; align-items: center; gap: 10px;">
+    ✨ Enter a Steam review to analyze its sentiment. For more information, see the dataset used at:
+        <a href="https://www.kaggle.com/datasets/filipkin/steam-reviews" target="_blank">
+            <img src="https://img.shields.io/badge/Kaggle-Steam%20Reviews-blue?logo=kaggle" alt="Kaggle">
+        </a>
+        |
+        <a href="https://github.com/alyzbane/gradio-sentimental-analysis-ml" target="_blank">
+            <img src="https://img.shields.io/badge/GitHub-Repository-blue?logo=github" alt="GitHub">
+        </a>
+    </div>
+    """, elem_classes="centered-md")
+
    
     chatbot = gr.Chatbot(
+        type="messages",
         label="History",
-        height=400
+        placeholder="Share a though about video game 🎮👇",
+        height=400,
     )
     
     with gr.Row():
@@ -86,7 +89,7 @@ with gr.Blocks(theme=gr.themes.Default(), title="Gaming Sentiment Chatbot", css=
         model_choice = gr.Dropdown(
             choices=list(models.keys()),
             value="MultinomialNB",
-            label="Select Model for Analysis",
+            label=r"↓ Select Model for Analysis",
         )
     
     # Example messages
@@ -97,7 +100,7 @@ with gr.Blocks(theme=gr.themes.Default(), title="Gaming Sentiment Chatbot", css=
             "I can't believe how buggy this game is. Constant crashes and poor optimization.",
             "Decent game but nothing special. Might be worth it on sale.",
             "Best game I've played this year! The story is amazing!",
-            "1/10"
+            "this game is 1/10 at best. Waste of money"
         ],
         inputs=message,
         label="Example Messages"

requirements.txt

@@ -0,0 +1,3 @@
+gradio==5.9.1
+joblib==1.4.2
+scikit_learn==1.6.0