good

app.py

@@ -1,32 +1,35 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import wikipediaapi
 
-"""
-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
-"""
+# Initialize inference client for chat
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Initialize Wikipedia API
+wiki_wiki = wikipediaapi.Wikipedia('en')
 
+def search_wikipedia(query):
+    page = wiki_wiki.page(query)
+    if page.exists():
+        return page.summary
+    else:
+        return "No information found on that topic."
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    # Search Wikipedia for information
+    search_response = search_wikipedia(message)
+    
+    # Prepare the chat messages
     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]})
-
+    
     messages.append({"role": "user", "content": message})
-
+    
+    # Generate response from chat model
     response = ""
-
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -35,30 +38,31 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-
         response += token
-        yield response
+        yield response, search_response  # Return both responses
 
+# Gradio interface setup using Blocks
+with gr.Blocks() as demo:
+    gr.Markdown("## Chatbot with Wikipedia Search")
+    with gr.Row():
+        with gr.Column():
+            system_message = gr.Textbox(value="You are a friendly Chatbot named Tirth.", label="System message")
+            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)")
+        
+        with gr.Column():
+            chat_output = gr.Chatbox(label="Chat History")
+            user_input = gr.Textbox(placeholder="Type your message here...", label="Your Message")
+            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 named Tirth.", 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)",
-        ),
-    ],
-)
+    # Function to handle button click
+    def on_submit(message, history):
+        response, search_response = respond(message, history, system_message.value, max_tokens.value, temperature.value, top_p.value)
+        return history + [(message, response)], search_response
 
+    # Connect the button to the submission function
+    submit_btn.click(on_submit, inputs=[user_input, chat_output], outputs=[chat_output, gr.Textbox(label="Wikipedia Summary")])
 
 if __name__ == "__main__":
     demo.launch()