Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 from datasets import load_dataset
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 """
 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
@@ -9,12 +8,13 @@ For more information on `huggingface_hub` Inference API support, please check th
 
 #Update: Using a new base model
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+#client = InferenceClient("HuggingFaceH4/zephyr-7b-gemma-v0.1")
+#topic_model = BERTopic.load("MaartenGr/BERTopic_Wikipedia")
+# Train model
+#topic_model = BERTopic("english")
+#topics, probs = topic_model.fit_transform(docs)
 dataset = load_dataset("JustKiddo/KiddosVault")
 
-# Load the tokenizer and model for token display
-tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small") #Google's T5 Model
-model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
-
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -47,71 +47,25 @@ def respond(
         response += token
         yield response
 
-#My custom token generator
-def generate_tokens(text):
-    input = tokenizer(text, return_tensors="pt")
-    output = model.generate(**input)
-
-    input_ids = input["input_ids"].tolist()[0]
-    output_ids = output.tolist()[0]
-
-    formatted_output = [format(x, 'd') for x in output_ids]
-
-    input_tokens_str = tokenizer.convert_ids_to_tokens(input_ids)
-    #output_tokens_str = tokenizer.convert_tokens_to_ids(output_ids)
-
-    return " ".join(input_tokens_str), " ".join(formatted_output)
-
 """
 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 professional Mental Healthcare Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=6144, value=6144, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=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)",
+        ),
+    ],
+)
 
-#chatInterface = gr.ChatInterface(
-#    respond,
-#    additional_inputs=[
-#        gr.Textbox(value="You are a professional Mental Healthcare Chatbot.", label="System message"),
-#        gr.Slider(minimum=1, maximum=6144, value=6144, step=1, label="Max new tokens"),
-#        gr.Slider(minimum=0.1, maximum=4.0, value=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)",
-#        ),
-#    ],
-#)
-
-with gr.Blocks() as demo:
-    with gr.Column():
-        gr.ChatInterface(
-            respond,
-            additional_inputs=[
-                gr.Textbox(value="You are a professional Mental Healthcare Chatbot.", label="System message"),
-                gr.Slider(minimum=1, maximum=6144, value=6144, step=1, label="Max new tokens"),
-                gr.Slider(minimum=0.1, maximum=4.0, value=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)",
-                ),
-            ],
-        )
-        
-    with gr.Row():
-        input_text = gr.Textbox(label="Input text")
-        input_tokens = gr.Textbox(label="Input tokens")
-        output_ids = gr.Textbox(label="Output tokens")
-
-        def update_tokens(input_text):
-            input_tokens_str, output_ids = generate_tokens(input_text)
-            return input_tokens_str, output_ids
-
-        input_text.change(update_tokens, 
-                          inputs=input_text,
-                          outputs=[input_tokens, output_ids])
 
 if __name__ == "__main__":
-    demo.launch(debug=True, share=True)
+    demo.launch(debug=True)