Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load pretrained model
+model_name = "microsoft/DialoGPT-medium"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Keep track of chat history
+chat_history_ids = None
+
+def chatbot(user_input):
+    global chat_history_ids
+
+    # Encode user message
+    new_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
+
+    # Append previous history if any
+    bot_input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1) if chat_history_ids is not None else new_input_ids
+
+    # Generate response
+    chat_history_ids = model.generate(
+        bot_input_ids,
+        max_length=1000,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95
+    )
+
+    # Decode reply
+    reply = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return reply
+
+# Create simple Gradio UI
+demo = gr.Interface(
+    fn=chatbot,
+    inputs=gr.Textbox(label="👤 You:"),
+    outputs=gr.Textbox(label="🤖 Bot:"),
+    title="Mini Chatbot using DialoGPT",
+    description="Chat with a pretrained conversational model built using Hugging Face Transformers + Gradio."
+)
+
+demo.launch()