Update app.py

app.py

@@ -173,52 +173,52 @@ with gr.Blocks() as app:
         combine_button.click(combine_datasets, outputs=[combined_output, download_output])
 
     with gr.Tab("Train and Chat"):
-    gr.Markdown("**Train a chatbot with a selected dataset and interact with it.**")
-
-    chat_dataset = gr.Textbox(
-        label="Dataset for Training",
-        placeholder="Paste or load a dataset for training.",
-        lines=5,
-    )
-    train_button = gr.Button("Train Chatbot")
-    chatbot = gr.Chatbot(label="Chat with Trained Bot", type="messages")
-    user_input = gr.Textbox(
-        label="Your Message",
-        placeholder="Type a message and press Enter...",
-        lines=1,
-    )
-
-    # Persistent system message with dataset knowledge
-    system_message = {"system": "You are a bot trained on the following dataset:"}
-    bot_knowledge = {"dataset": None}
-
-    # Train the chatbot by setting the dataset
-    def train_chatbot(dataset):
-        bot_knowledge["dataset"] = dataset
-        return "Chatbot trained successfully!"
-
-    # Chat function for handling user messages
-    def chat_with_bot(history, user_message):
-        if not bot_knowledge["dataset"]:
-            return history + [{"role": "bot", "content": "No dataset loaded. Please train the bot first."}]
-
-        # Append user input to history
-        history.append({"role": "user", "content": user_message})
-
-        # Generate bot response based on the dataset
-        prompt = f"{bot_knowledge['dataset']} {user_message}"
-        response = client.text_generation(prompt=prompt, max_new_tokens=128)["generated_text"]
-
-        # Append bot response to history
-        history.append({"role": "bot", "content": response})
-        return history
-
-    # Train button event
-    train_button.click(train_chatbot, inputs=[chat_dataset], outputs=None)
-
-    # User input submission event
-    user_input.submit(
-        chat_with_bot, inputs=[chatbot, user_input], outputs=chatbot
-    )
-
+        gr.Markdown("**Train a chatbot with a selected dataset and interact with it.**")
+    
+        chat_dataset = gr.Textbox(
+            label="Dataset for Training",
+            placeholder="Paste or load a dataset for training.",
+            lines=5,
+        )
+        train_button = gr.Button("Train Chatbot")
+        chatbot = gr.Chatbot(label="Chat with Trained Bot", type="messages")
+        user_input = gr.Textbox(
+            label="Your Message",
+            placeholder="Type a message and press Enter...",
+            lines=1,
+        )
+    
+        # Persistent system message with dataset knowledge
+        system_message = {"system": "You are a bot trained on the following dataset:"}
+        bot_knowledge = {"dataset": None}
+    
+        # Train the chatbot by setting the dataset
+        def train_chatbot(dataset):
+            bot_knowledge["dataset"] = dataset
+            return "Chatbot trained successfully!"
+    
+        # Chat function for handling user messages
+        def chat_with_bot(history, user_message):
+            if not bot_knowledge["dataset"]:
+                return history + [{"role": "bot", "content": "No dataset loaded. Please train the bot first."}]
+    
+            # Append user input to history
+            history.append({"role": "user", "content": user_message})
+    
+            # Generate bot response based on the dataset
+            prompt = f"{bot_knowledge['dataset']} {user_message}"
+            response = client.text_generation(prompt=prompt, max_new_tokens=128)["generated_text"]
+    
+            # Append bot response to history
+            history.append({"role": "bot", "content": response})
+            return history
+    
+        # Train button event
+        train_button.click(train_chatbot, inputs=[chat_dataset], outputs=None)
+
+        # User input submission event
+        user_input.submit(
+            chat_with_bot, inputs=[chatbot, user_input], outputs=chatbot
+        )
+    
 app.launch()