Create app.py

app.py

@@ -0,0 +1,25 @@
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# Load a lightweight conversational model (CPU-friendly)
+model_name = "microsoft/DialoGPT-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+chatbot = pipeline("text-generation", model=model, tokenizer=tokenizer, device=-1)  # device=-1 forces CPU
+
+def respond(message, history):
+    # Format history for DialoGPT
+    history_str = "\n".join([f"User: {h[0]}\nBot: {h[1]}" for h in history])
+    input_text = f"{history_str}\nUser: {message}\nBot:"
+    
+    response = chatbot(input_text, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+    bot_reply = response[0]['generated_text'].split("Bot:")[-1].strip()
+    return bot_reply
+
+gr.ChatInterface(
+    respond,
+    title="My Chatbot",
+    description="A simple CPU-friendly chatbot using DialoGPT-small.",
+    examples=["Hello!", "How are you?", "Tell me a joke."]
+).launch()