Update app.py

app.py

@@ -11,32 +11,32 @@ top_k = 10
 model_info = {
     "single_conversation_withGPTdata_bs256": {
         'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/singleConversation_withGPTdata_bs256.pt',
-        'description': "Trained on Facebook Emotion Dialogues dataset with additional GPT data, using a batch size of 256.",
+        'description': " We trained this model on on Facebook Emotion Dialogues dataset with additional GPT data, using a batch size of 256.",
         'logo': '🧃'
     },
     "single_conversation_withGPTdata_withoutemotion": {
         'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/singleConversation_withGPTdata_withoutemotion.pt',
-        'description': "Trained on Facebook Emotion Dialogues dataset with GPT data, excluding emotion tag.",
+        'description': " We trained this model on Facebook Emotion Dialogues dataset with GPT data, excluding emotion tag, using a default batch size of 64.",
         'logo': '🧉'
     },
     "single_conversation_withcontext": {
         'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/singleConversation_withcontext.pt',
-        'description': "Trained on Facebook Emotion Dialogues dataset with context included for improved conversational understanding.",
+        'description': "Trained on Facebook Emotion Dialogues dataset with context included for improved conversational understanding, using a default batch size of 64.",
         'logo': '🍹'
     },
     "single_conversation_withemotion": {
         'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/singleConversation_withemotion.pt',
-        'description': "Trained on Facebook Emotion Dialogues dataset, retaining emotion annotations for each conversation.",
+        'description': "Trained on Facebook Emotion Dialogues dataset, retaining emotion annotations for each conversation, using a default batch size of 64.",
         'logo': '🍺'
     },
     "single_conversation_withoutemotion": {
         'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/singleConversation_withoutemotion.pt',
-        'description': "Trained on Facebook Emotion Dialogues dataset, excluding emotion annotations for simpler conversations.",
+        'description': "Trained on Facebook Emotion Dialogues dataset, excluding emotion annotations for simpler conversations, using a default batch size of 64.",
         'logo': '🍷'
     },
     "whole_conversation_withoutemotion": {
         'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/wholeConversation_withoutemotion.pt',
-        'description': "Trained on entire conversations from the Facebook Emotion Dialogues dataset, excluding tags other than <bot> and <human>.",
+        'description': "Trained on entire conversations from the Facebook Emotion Dialogues dataset, excluding tags other than <bot> and <human>,, using a default batch size of 64",
         'logo': '🍵'
     }
 }
@@ -54,7 +54,7 @@ encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"})
 decode = lambda l: enc.decode(l)
 
 
-def predict(input: str,  history: list = None) -> tuple:
+def predict(input_hints, input: str,  history: list = None) -> tuple:
     if history is None:
         history = []  # Initialize history if not provided
     # Generate a response using the respond function
@@ -70,10 +70,12 @@ def predict(input: str,  history: list = None) -> tuple:
         top_k=top_k,
     )
     
-    response = response_data[1]  # Extract bot's response
+    response = response_data[1]
+    full_output =  response_data[2]
+    print(f"full_output: {full_output}")
     history.append((input, response))  # Append the user input and bot response to history
 
-    return history, history  # Return updated history twice (for chatbot and state)
+    return history, history, full_output  # Return updated history twice (for chatbot and state)
 
 def prepare_model(selected_model):
     global selected_model_name
@@ -85,9 +87,20 @@ def prepare_model(selected_model):
     description = model_info[selected_model]['description']
     return f"## {logo}Model Information\n<br>Model_name: {selected_model}\n<br>Description: {description}"
 
-default_model_info = f"## 🍭Model Information\n<br>Model_name: Name of the model\n<br>Description: How we train the model"
-app = gr.Blocks()
+def update_chat_with_model_selection(model, chat_history):
+    # Add a message about the selected model
+    if chat_history is None:
+        chat_history = []
+    chat_history.append(
+        (None, 
+         f'<span style="background-color: #FFD700; padding: 4px; border-radius: 4px;">Now you are chatting with <strong>{model}</strong></span>')
+    )
+    return chat_history
 
+
+default_model_info = f"## 🍭Model Information\n<br>Model_name: single_conversation_withGPTdata_bs256\n<br>Description: We trained this model on Facebook Emotion Dialogues dataset with additional GPT data, using a batch size of 256."
+app = gr.Blocks()
+full_output = " "
 with app:
     gr.Markdown("# 🫂 Chatbot for ML Project\n### 🤗 Chat with your  chatbot!")
     # Model Parameters interface
@@ -100,18 +113,35 @@ with app:
     inp.change(prepare_model, inp, out)
 
     # Chatbot interface
+    chatbot = gr.Chatbot(
+        label="Chatbot Response",
+        avatar_images=(
+            None,  # User avatar (None for default)
+            "https://em-content.zobj.net/source/twitter/376/hugging-face_1f917.png"  # Assistant avatar
+        )
+    )
+    user_input = gr.Textbox(lines=2, placeholder="Enter your message here...", label="User Input")
+    state = gr.State([])
+    debug_result = gr.Textbox(label="Debug: Full model output",value=full_output)
+    input_hints = gr.Markdown("## 📝 Input Hints\n<br>1. Select a model from the dropdown list. \n<br> 2. Type your message in the text box, please try to input a complete sentence.\n<br> 3. Fill the [form](https://forms.office.com/e/PuTy4jrcQD) here to help us evaluate the model")
     chat_interface = gr.Interface(
         fn=predict,
         inputs=[
-            gr.Textbox(lines=2, placeholder="Enter your message here...", label="User Input"),
-            gr.State(),  # Maintain conversation state
+            input_hints,
+            user_input,
+            state,  # Maintain conversation state
+            
         ],
         outputs=[
-            gr.Chatbot(label="Chatbot Response"),  # Display responses in chat format
-            gr.State()  # Return the updated state
+            chatbot,
+            state,
+            debug_result      
         ],
         description="Your AI-based chatbot powered by selected models!"
     )
+    
+    inp.change(fn=update_chat_with_model_selection, inputs=[inp, state], outputs=[chatbot])
+    
     #TODO: add emotion/context here
 if __name__ == "__main__":
     app.launch(share=True)