Update app.py

app.py

@@ -1,64 +1,108 @@
+import os
+import torch
 import gradio as gr
+import spaces
 from huggingface_hub import InferenceClient
+from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain.prompts import PromptTemplate
 
-"""
-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")
+# Configure ZeroGPU client
+client = InferenceClient("meta-llama/Llama-3.2-3B-Instruct")
 
+# Initialize embeddings
+embeddings = HuggingFaceInstructEmbeddings(
+    model_name="hkunlp/instructor-base",
+    model_kwargs={"device": "cpu"}  # Use CPU for Spaces
+)
+
+# Load the persisted database
+db = Chroma(
+    persist_directory="db",
+    embedding_function=embeddings
+)
 
+# Prompt templates
+DEFAULT_SYSTEM_PROMPT = """
+You are a ROS2 expert assistant. Based on the information provided in the context, answer questions 
+accurately and concisely. If the information is not in the context, acknowledge that you don't know.
+""".strip()
+
+@spaces.GPU(duration=60)
 def respond(
     message,
-    history: list[tuple[str, str]],
+    history,
     system_message,
     max_tokens,
     temperature,
     top_p,
 ):
-    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})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
+    try:
+        # Retrieve relevant context
+        docs = db.similarity_search(message, k=2)
+        context = "\n".join([doc.page_content for doc in docs])
+        
+        # Build 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]})
+        
+        # Add context to the user message
+        augmented_message = f"Context: {context}\n\nQuestion: {message}"
+        messages.append({"role": "user", "content": augmented_message})
+        
+        # Stream the response
+        response = ""
+        for message in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = message.choices[0].delta.content
+            response += token
+            yield response
+            
+    except Exception as e:
+        yield f"An error occurred: {str(e)}"
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Create Gradio interface
 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.Textbox(
+            value=DEFAULT_SYSTEM_PROMPT,
+            label="System message"
+        ),
+        gr.Slider(
+            minimum=1,
+            maximum=2048,
+            value=500,
+            step=1,
+            label="Max new tokens"
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=4.0,
+            value=0.1,
+            step=0.1,
+            label="Temperature"
+        ),
         gr.Slider(
             minimum=0.1,
             maximum=1.0,
             value=0.95,
             step=0.05,
-            label="Top-p (nucleus sampling)",
+            label="Top-p (nucleus sampling)"
         ),
     ],
+    title="ROS2 Expert Assistant",
+    description="Ask questions about ROS2, navigation, and robotics. I'll answer based on my knowledge base.",
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()