Update app.py

app.py

@@ -1,64 +1,34 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from Rag_conversation import rag_chain  # Import the RAG pipeline from your script
+from langchain_core.messages import HumanMessage, SystemMessage
 
-"""
-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")
+# Function to process user messages
+def chatbot(user_message, history):
+    """
+    :param user_message: The latest user message
+    :param history: A list of (user, ai) tuples from Gradio chat
+    :return: (bot_reply, updated_history)
+    """
+    chat_history = []
+    for h in history:
+        chat_history.append(HumanMessage(content=h[0]))  # User message
+        chat_history.append(SystemMessage(content=h[1]))  # AI response
 
+    chat_history.append(HumanMessage(content=user_message))  # Add new message
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    # Get response from RAG-based chatbot
+    result = rag_chain.invoke({"input": user_message, "chat_history": chat_history})
+    bot_reply = result["answer"]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    return bot_reply
 
-    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
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Create Gradio UI
 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)",
-        ),
-    ],
+    chatbot,
+    title="Binghamton RAG Chatbot",
+    description="Ask questions about Binghamton University.",
 )
 
-
 if __name__ == "__main__":
     demo.launch()