app.py

import gradio as gr
from transformers import BlenderbotSmallTokenizer, BlenderbotSmallForConditionalGeneration


model_name = "facebook/blenderbot-90M"
tokenizer = BlenderbotSmallTokenizer.from_pretrained(model_name)
model = BlenderbotSmallForConditionalGeneration.from_pretrained(model_name)


def chat_with_bot(message, history):
    if not message:
        return "Hi there! 👋 Ask me something to get started."

   
    conversation = ""
    if history:
        for turn in history:
            role = turn.get("role")
            content = turn.get("content")
            if role == "user":
                conversation += f"User: {content}\n"
            elif role == "assistant":
                conversation += f"Bot: {content}\n"

    conversation += f"User: {message}\nBot:"

   
    inputs = tokenizer(
        conversation,
        return_tensors="pt",
        truncation=True,
        padding="max_length",
        max_length=512,
    )
    reply_ids = model.generate(**inputs, max_length=120)
    reply = tokenizer.decode(reply_ids[0], skip_special_tokens=True)
    return reply


initial_message = [
    {"role": "assistant", "content": "👋 Hello! I’m your chatbot. Ask me anything to start our conversation!"}
]


demo = gr.ChatInterface(
    fn=chat_with_bot,
    title="🤖 Mini Chatbot (Facebook BlenderBot-90M)",
    description="Hi 👋 I’m a small conversational chatbot powered by Facebook’s BlenderBot-90M.",
    theme="soft",
    type="messages",
    examples=["Hello!", "Tell me a fun fact", "How are you today?"],
)


if __name__ == "__main__":
    demo.launch(share=True)