Update app.py

app.py

@@ -1,64 +1,89 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Import the Carmen module. (Ensure that the repository is installed and accessible.)
+from carmen.sentience import analyze_sentience
 
+# Initialize the chat client.
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+def chat_and_sentience(message, history, system_message, max_tokens, temperature, top_p):
+    # Prepare messages for the LLM conversation.
     messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
     messages.append({"role": "user", "content": message})
-
+    
     response = ""
-
-    for message in client.chat_completion(
+    # Generate chat response via streaming.
+    for chat in client.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        token = message.choices[0].delta.content
-
+        token = chat.choices[0].delta.content
         response += token
-        yield response
+        # Update the UI with the intermediate chat history; sentiment analysis hasn't run yet.
+        yield [history + [(message, response)], None]
+    
+    # Once the full response is assembled, perform sentience analysis using Carmen.
+    # The function analyze_sentience is assumed to return a dictionary or list of sentiment scores/labels.
+    sentiment_results = analyze_sentience(response)
+    
+    # Format the results for display. Adjust the formatting based on the actual output of analyze_sentience.
+    if isinstance(sentiment_results, dict):
+        sentiment_str = "\n".join([f"{k}: {v:.2f}" for k, v in sentiment_results.items()])
+    elif isinstance(sentiment_results, list):
+        sentiment_str = "\n".join([f"{item['label']}: {item['score']:.2f}" for item in sentiment_results])
+    else:
+        sentiment_str = str(sentiment_results)
+    
+    # Yield the final state: updated chat history and the sentiment analysis result.
+    yield [history + [(message, response)], sentiment_str]
 
+# Build the UI with gr.Blocks.
+with gr.Blocks() as demo:
+    with gr.Row():
+        chatbot = gr.Chatbot(label="Chat")
+    with gr.Row():
+        sentiment_box = gr.Textbox(
+            label="Sentience Moment Scanner",
+            lines=4,
+            placeholder="Emotion analysis will appear here..."
+        )
+    with gr.Row():
+        message_input = gr.Textbox(label="Your Message")
+    with gr.Row():
+        system_message_input = gr.Textbox(value="You are a friendly Chatbot.", label="System Message")
+    with gr.Row():
+        max_tokens_slider = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens")
+    with gr.Row():
+        temperature_slider = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+    with gr.Row():
+        top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+    submit_btn = gr.Button("Send")
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    # Use a state to track conversation history.
+    state = gr.State([])
 
+    # Wire up the events: on click or pressing enter the chat and sentiment analysis runs.
+    submit_btn.click(
+         chat_and_sentience,
+         inputs=[message_input, state, system_message_input, max_tokens_slider, temperature_slider, top_p_slider],
+         outputs=[chatbot, sentiment_box],
+         show_progress=True
+    )
+    message_input.submit(
+         chat_and_sentience,
+         inputs=[message_input, state, system_message_input, max_tokens_slider, temperature_slider, top_p_slider],
+         outputs=[chatbot, sentiment_box],
+         show_progress=True
+    )
 
 if __name__ == "__main__":
     demo.launch()