Update app.py

app.py

@@ -1,12 +1,14 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import pipeline
 
-"""
-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 the InferenceClient with the Zephyr model
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# Use a pipeline for the ParlBERT model for fill-mask
+mask_pipe = pipeline("fill-mask", model="InfAI/parlbert-german-law")
 
+# Define the function for chat completion
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -39,26 +41,39 @@ def respond(
         response += token
         yield response
 
+# Function to handle mask filling
+def fill_mask_function(text):
+    return mask_pipe(text)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=respond,
+    inputs=[
+        gr.Textbox(label="Enter your message"),
+        gr.State(),  # History
         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)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
+    outputs=[
+        gr.Textbox(label="Response"),
+    ],
+    title="Zephyr and ParlBERT Chatbot",
+    description="This chatbot uses Zephyr-7B model and ParlBERT (German Law) for conversation and masked word predictions."
 )
 
+# Create a separate Gradio interface for the fill-mask model
+mask_demo = gr.Interface(
+    fn=fill_mask_function,
+    inputs=gr.Textbox(label="Input text with [MASK] token"),
+    outputs=gr.JSON(label="Model output"),
+    live=True,
+    title="InfAI ParlBERT - German Law",
+    description="Enter a sentence with a [MASK] token to get predictions from the InfAI ParlBERT model trained on German law text."
+)
 
+# Launch both interfaces
 if __name__ == "__main__":
     demo.launch()
+    mask_demo.launch()