Varun-Background Knowledge Report

app.py

@@ -1,11 +1,12 @@
 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")
+# Step 1: Read your background info
+with open("BACKGROUND.md", "r", encoding="utf-8") as f:
+    background_text = f.read()
 
+# Step 2: Set up your InferenceClient (same as before)
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 def respond(
     message,
@@ -16,7 +17,7 @@ def respond(
     top_p,
 ):
     messages = [{"role": "system", "content": system_message}]
-
+    
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
@@ -27,38 +28,39 @@ def respond(
 
     response = ""
 
-    for message in client.chat_completion(
+    for msg in client.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        token = message.choices[0].delta.content
-
+        token = msg.choices[0].delta.content
         response += token
+        # 'yield' returns partial responses for streaming
         yield response
 
+# Step 3: Build a Gradio Blocks interface with two Tabs
+with gr.Blocks() as demo:
+    # (A) First Tab: Chat Interface
+    with gr.Tab("GPT Chat Agent"):
+        gr.Markdown("## Welcome to Varun's GPT Agent")
+        gr.Markdown("Feel free to ask questions about Varun’s journey, skills, and more!")
+        chat = 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)"),
+            ],
+        )
 
-"""
-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)",
-        ),
-    ],
-)
-
+    # (B) Second Tab: Background Document
+    with gr.Tab("Varun's Background"):
+        gr.Markdown("# About Varun")
+        gr.Markdown(background_text)
 
+# Step 4: Launch
 if __name__ == "__main__":
     demo.launch()