Update app.py

app.py

@@ -1,9 +1,24 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from datetime import datetime
+import json
 
 # Initialize the InferenceClient with the model ID from Hugging Face
 client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta")
 
+# Load chat history from a file if it exists
+def load_chat_history(filename="chat_history.json"):
+    try:
+        with open(filename, "r") as file:
+            return json.load(file)
+    except FileNotFoundError:
+        return []
+
+# Save chat history to a file
+def save_chat_history(history, filename="chat_history.json"):
+    with open(filename, "w") as file:
+        json.dump(history, file)
+
 def generate_response(
     messages: list[dict],
     max_tokens: int,
@@ -23,9 +38,7 @@ def generate_response(
         str: The AI's response as it is generated.
     """
     response = ""
-
     try:
-        # Generate a response from the model with streaming
         for message in client.chat_completion(
             messages=messages,
             max_tokens=max_tokens,
@@ -60,9 +73,7 @@ def build_messages(system_message: str, history: list[tuple[str, str]], user_mes
         if assistant_response:
             messages.append({"role": "assistant", "content": assistant_response})
 
-    # Add the latest user message to the conversation
     messages.append({"role": "user", "content": user_message})
-    
     return messages
 
 def respond(
@@ -90,7 +101,23 @@ def respond(
     messages = build_messages(system_message, history, message)
     yield from generate_response(messages, max_tokens, temperature, top_p)
 
-# Define the UI layout with improved structure
+def update_chat_history(user_message: str, assistant_response: str, history: list[tuple[str, str]]) -> list[tuple[str, str]]:
+    """
+    Updates the chat history with the latest user message and assistant response.
+
+    Args:
+        user_message (str): The latest user message.
+        assistant_response (str): The response generated by the assistant.
+        history (list): The existing chat history.
+
+    Returns:
+        list: The updated chat history.
+    """
+    history.append((user_message, assistant_response))
+    save_chat_history(history)
+    return history
+
+# Define the UI layout with additional features
 with gr.Blocks() as demo:
     gr.Markdown("# 🧠 AI Chatbot Interface")
     gr.Markdown("### Customize your AI Chatbot's behavior and responses.")
@@ -105,6 +132,7 @@ with gr.Blocks() as demo:
             max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
             temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
             top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+            theme = gr.Radio(choices=["Light", "Dark"], label="Theme", value="Light")
     
     with gr.Row():
         chatbot = gr.Chatbot()
@@ -126,6 +154,8 @@ with gr.Blocks() as demo:
             message = gr.Textbox(label="Your message:", lines=1)
             submit_btn = gr.Button("Send")
             clear_btn = gr.Button("Clear Chat")
+            feedback = gr.Textbox(label="Feedback:", lines=1)
+            submit_feedback = gr.Button("Submit Feedback")
 
     # Handle sample prompt selection
     def update_message(prompt: str) -> str:
@@ -134,10 +164,16 @@ with gr.Blocks() as demo:
     sample_prompt.change(fn=update_message, inputs=sample_prompt, outputs=message)
     
     # Update the chatbot with the new message and response
+    def handle_send(message: str, system_message: str, max_tokens: int, temperature: float, top_p: float):
+        history = load_chat_history()
+        response = list(respond(message, history, system_message, max_tokens, temperature, top_p))[0]
+        history = update_chat_history(message, response, history)
+        return response, history
+
     submit_btn.click(
-        fn=respond, 
-        inputs=[message, chatbot, system_message, max_tokens, temperature, top_p], 
-        outputs=[chatbot],
+        fn=handle_send, 
+        inputs=[message, system_message, max_tokens, temperature, top_p], 
+        outputs=[chatbot, gr.State()],
         show_progress=True
     )
     
@@ -146,6 +182,14 @@ with gr.Blocks() as demo:
         return []
 
     clear_btn.click(fn=clear_chat, inputs=None, outputs=chatbot)
+    
+    # Handle feedback submission
+    def submit_user_feedback(feedback: str):
+        # In a real application, you would save this feedback to a database or file
+        print(f"Feedback received: {feedback}")
+        return "Thank you for your feedback!"
+
+    submit_feedback.click(fn=submit_user_feedback, inputs=feedback, outputs=[gr.Textbox(value="Feedback submitted! Thank you.", lines=1, placeholder="")])
 
 # Launch the Gradio interface
 if __name__ == "__main__":