Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model_name = "microsoft/DialoGPT-medium"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Store conversation history
+chat_history_ids = None
+
+def respond(message, history):
+    global chat_history_ids
+
+    # Encode user input
+    new_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors='pt')
+
+    # Append to chat history
+    if chat_history_ids is not None:
+        bot_input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1)
+    else:
+        bot_input_ids = 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=100,
+        top_p=0.7,
+        temperature=0.8
+    )
+
+    # Decode response
+    response = tokenizer.decode(
+        chat_history_ids[:, bot_input_ids.shape[-1]:][0],
+        skip_special_tokens=True
+    )
+
+    return response
+
+# Create Gradio interface
+demo = gr.ChatInterface(
+    fn=respond,
+    title="Dialogue System using DialoGPT",
+    description="A simple conversational AI built with HuggingFace Transformers and Gradio."
+)
+
+if __name__ == "__main__":
+    demo.launch()