Update app.py

app.py

@@ -1,12 +1,39 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import json
+import os
 
 """
 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")
 
+# Persistent memory and knowledge base setup
+memory_file = "chat_memory.json"
+knowledge_base = {
+    "AI": "Artificial Intelligence is a branch of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence.",
+    "Quantum Computing": "Quantum computing is a type of computation that uses quantum mechanics to process information in ways classical computers cannot.",
+}
 
+# Load memory from file
+def load_memory():
+    if os.path.exists(memory_file):
+        with open(memory_file, "r") as f:
+            return json.load(f)
+    return []
+
+# Save memory to file
+def save_memory(memory):
+    with open(memory_file, "w") as f:
+        json.dump(memory, f)
+
+# Append to memory
+def update_memory(conversation):
+    memory = load_memory()
+    memory.append(conversation)
+    save_memory(memory)
+
+# Response generation with memory and knowledge base integration
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -25,8 +52,15 @@ def respond(
 
     messages.append({"role": "user", "content": message})
 
-    response = ""
+    # Check for answers in the knowledge base
+    if message in knowledge_base:
+        response = knowledge_base[message]
+        update_memory((message, response))
+        yield response
+        return
 
+    # Generate response from AI
+    response = ""
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -35,30 +69,65 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-
         response += token
         yield response
 
+    # Update memory
+    update_memory((message, response))
 
-"""
-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)",
-        ),
-    ],
-)
 
+# Gradio interface with enhanced functionality
+def add_to_knowledge_base(key, value):
+    knowledge_base[key] = value
+    return f"Added to knowledge base: {key} -> {value}"
+
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("# Advanced Chatbot with Memory and Knowledge Base")
+
+    with gr.Tab("Chat"):
+        chatbot = gr.ChatInterface(
+            respond,
+            additional_inputs=[
+                gr.Textbox(value="You are an advanced AI 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)",
+                ),
+            ],
+        )
+
+    with gr.Tab("Knowledge Base"):
+        gr.Markdown("### Manage the Knowledge Base")
+        kb_key = gr.Textbox(label="Key", placeholder="Enter the topic or question")
+        kb_value = gr.Textbox(label="Value", placeholder="Enter the explanation or answer")
+        add_kb_button = gr.Button("Add to Knowledge Base")
+        kb_output = gr.Textbox(label="Knowledge Base Output")
+
+        add_kb_button.click(add_to_knowledge_base, [kb_key, kb_value], kb_output)
+
+    with gr.Tab("Memory"):
+        gr.Markdown("### Conversation Memory")
+        memory_display = gr.Textbox(label="Conversation Memory", lines=10)
+        refresh_memory = gr.Button("Refresh Memory")
+        clear_memory = gr.Button("Clear Memory")
+
+        def display_memory():
+            return json.dumps(load_memory(), indent=2)
+
+        def clear_memory_func():
+            save_memory([])
+            return "Memory Cleared!"
+
+        refresh_memory.click(display_memory, outputs=memory_display)
+        clear_memory.click(clear_memory_func, outputs=memory_display)
 
 if __name__ == "__main__":
     demo.launch()