Update app.py

app.py

@@ -1,127 +1,83 @@
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-
-# # 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("google/gemma-2-2b-jpn-it")
-# # HuggingFaceH4/zephyr-7b-beta
-# def respond(
-#     message,
-#     history: list[dict],
-#     system_message: str,
-#     max_tokens: int,
-#     temperature: float,
-#     top_p: float,
-# ):
-#     if history is None:
-#         history = []
-
-#     # Include background text as part of the system message for context
-#     combined_system_message = f"{system_message}\n\n### Background Information ###\n{background_text}"
-
-#     # Start building the conversation history
-#     messages = [{"role": "system", "content": combined_system_message}]
-    
-#     # Add conversation history
-#     for interaction in history:
-#         if "user" in interaction:
-#             messages.append({"role": "user", "content": interaction["user"]})
-#         if "assistant" in interaction:
-#             messages.append({"role": "assistant", "content": interaction["assistant"]})
-
-#     # Add the latest user message
-#     messages.append({"role": "user", "content": message})
-
-#     # Generate response
-#     response = ""
-#     for msg in client.chat_completion(
-#         messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-        
-#         token = msg.choices[0].delta.content
-#         response += token
-#         yield response
-#     print("----- SYSTEM MESSAGE -----")
-#     print(messages[0]["content"])
-#     print("----- FULL MESSAGES LIST -----")
-#     for m in messages:
-#         print(m)
-#     print("-------------------------")
-    
-# # Step 3: Build a Gradio Blocks interface with two Tabs
-# with gr.Blocks() as demo:
-#     # Tab 1: GPT Chat Agent
-#     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(
-#             fn=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)"),
-#             ],
-#             type="messages",  # Specify message type
-#         )
-
-#     # # Tab 2: Background Document
-#     # with gr.Tab("Varun's Background"):
-#     #     gr.Markdown("# About Varun")
-#     #     gr.Markdown(background_text)
-
-# # Step 4: Launch
-# if __name__ == "__main__":
-#     demo.launch()
-
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-client = InferenceClient("google/gemma-2-2b-jpn-it")
-
-def respond(message, history, system_message, max_tokens, temperature, top_p):
+# 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("meta-llama/Llama-3.2-1B")
+# HuggingFaceH4/zephyr-7b-beta
+def respond(
+    message,
+    history: list[dict],
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
+):
     if history is None:
         history = []
 
-    prompt = f"{system_message}\n\n# Background...\n\n"  # etc.
-    # Build up your prompt from history...
+    # Include background text as part of the system message for context
+    combined_system_message = f"{system_message}\n\n### Background Information ###\n{background_text}"
+
+    # Start building the conversation history
+    messages = [{"role": "system", "content": combined_system_message}]
+    
+    # Add conversation history
+    for interaction in history:
+        if "user" in interaction:
+            messages.append({"role": "user", "content": interaction["user"]})
+        if "assistant" in interaction:
+            messages.append({"role": "assistant", "content": interaction["assistant"]})
+
+    # Add the latest user message
+    messages.append({"role": "user", "content": message})
 
+    # Generate response
     response = ""
-    for chunk in client.text_generation(
-        prompt=prompt,
-        max_new_tokens=max_tokens,
+    for msg in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
         temperature=temperature,
         top_p=top_p,
-        stream=True,
     ):
-        # 'chunk' is a string of newly generated text.
-        response += chunk
+        
+        token = msg.choices[0].delta.content
+        response += token
         yield response
-
-    # (Optional) log the final prompt
-    print("PROMPT:", prompt)
-
-
+    print("----- SYSTEM MESSAGE -----")
+    print(messages[0]["content"])
+    print("----- FULL MESSAGES LIST -----")
+    for m in messages:
+        print(m)
+    print("-------------------------")
+    
+# Step 3: Build a Gradio Blocks interface with two Tabs
 with gr.Blocks() as demo:
-    with gr.Tab("Gemma Chat Agent"):
+    # Tab 1: GPT Chat Agent
+    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(
             fn=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"),
+                gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
             ],
-            type="messages",
+            type="messages",  # Specify message type
         )
 
+    # # Tab 2: Background Document
+    # with gr.Tab("Varun's Background"):
+    #     gr.Markdown("# About Varun")
+    #     gr.Markdown(background_text)
+
+# Step 4: Launch
 if __name__ == "__main__":
     demo.launch()