adding datasets

app.py

@@ -1,13 +1,23 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from datasets import load_dataset
 
-"""
-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("mistralai/Mistral-7B-Instruct-v0.3")
+# ✅ Load the datasets
+datasets = {
+    "sales": load_dataset("goendalf666/sales-conversations"),
+    "blended": load_dataset("blended_skill_talk"),
+    "dialog": load_dataset("daily_dialog"),
+    "multiwoz": load_dataset("multi_woz_v22"),
+}
 
+# Optional: Print dataset names and sizes
+for name, dataset in datasets.items():
+    print(f"{name}: {len(dataset['train'])} examples")
 
+# Initialize the model client (use correct model for chatbot)
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
 
+# Chatbot response function
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -28,22 +38,19 @@ def respond(
 
     response = ""
 
-    for message in client.chat_completion(
+    for message in client.chat_completions(
         messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        token = message.choices[0].delta.content
-
+        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
-"""
+# Gradio interface for chatbot
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
@@ -60,6 +67,6 @@ demo = gr.ChatInterface(
     ],
 )
 
-
+# Launch Gradio app
 if __name__ == "__main__":
     demo.launch()