Update app.py

app.py

@@ -1,64 +1,31 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    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
-"""
-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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+
+# Load the Blenderbot model and tokenizer
+MODEL_NAME = "facebook/blenderbot-3B"
+tokenizer = BlenderbotTokenizer.from_pretrained(MODEL_NAME)
+model = BlenderbotForConditionalGeneration.from_pretrained(MODEL_NAME)
+
+def chatbot_response(user_input, chat_history=[]):
+    """Generates a response from Blenderbot based on user input."""
+    inputs = tokenizer(user_input, return_tensors="pt")
+    reply_ids = model.generate(**inputs)
+    response = tokenizer.decode(reply_ids[0], skip_special_tokens=True)
+    chat_history.append((user_input, response))
+    return response, chat_history
+
+# Set up Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 Blenderbot 3B Chatbot")
+    chatbot = gr.Chatbot()
+    user_input = gr.Textbox(label="Your message")
+    submit_btn = gr.Button("Send")
+    clear_btn = gr.Button("Clear Chat")
+    
+    chat_state = gr.State([])
+    
+    submit_btn.click(chatbot_response, inputs=[user_input, chat_state], outputs=[chatbot, chat_state])
+    clear_btn.click(lambda: ([], []), inputs=[], outputs=[chatbot, chat_state])
+
+# Launch the chatbot
+demo.launch()