Update qachatbot.py

qachatbot.py

@@ -1,30 +1,30 @@
-import gradio as gr
-from transformers import AutoTokenizer, TextIteratorStreamer
-
-# Define the models and their configurations
-model_name = "phi-2"  # Replace with the actual model name
-model_configuration = {
-    "toeknizer_kwargs": {SUPPORTED_LLM_MODELS[model_id.value]}  # Replace with the actual tokenizer configuration
-}
-
-# Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-tokenizer_kwargs = model_configuration.get("toeknizer_kwargs", {})
-
-# Define the Gradio interface
-def main():
-    with gr.Row():
-        with gr.Column(scale=4):
-            user_text = gr.Textbox(
-                placeholder="Write an email about an alpaca that likes flan",
-                label="User instruction",
-            )
-            model_output = gr.Textbox(label="Model response", interactive=False)
-            performance = gr.Textbox(label="Performance", lines=1, interactive=False)
-            with gr.Column(scale=1):
-                button_clear = gr.Button(value="Clear")
-                button_submit = gr.Button(value="Submit")
-
-# Run the Gradio interface
-iface = gr.Interface(fn=main, inputs=user_text, outputs=model_output, performance=performance, live=True)
+import gradio as gr
+from transformers import AutoTokenizer, TextIteratorStreamer
+
+# Define the models and their configurations
+model_name = "phi-2"  # Replace with the actual model name
+model_configuration = {
+    "toeknizer_kwargs": {'model_id': 'susnato/phi-2', 'prompt_template': 'Instruct:{instruction}\nOutput:'}  # Replace with the actual tokenizer configuration
+}
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer_kwargs = model_configuration.get("toeknizer_kwargs", {})
+
+# Define the Gradio interface
+def main():
+    with gr.Row():
+        with gr.Column(scale=4):
+            user_text = gr.Textbox(
+                placeholder="Write an email about an alpaca that likes flan",
+                label="User instruction",
+            )
+            model_output = gr.Textbox(label="Model response", interactive=False)
+            performance = gr.Textbox(label="Performance", lines=1, interactive=False)
+            with gr.Column(scale=1):
+                button_clear = gr.Button(value="Clear")
+                button_submit = gr.Button(value="Submit")
+
+# Run the Gradio interface
+iface = gr.Interface(fn=main, inputs=user_text, outputs=model_output, performance=performance, live=True)
 iface.launch()