update app.py

app.py

@@ -5,30 +5,30 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("ai4bharat/Airavata")
 model = AutoModelForCausalLM.from_pretrained("ai4bharat/Airavata")
 
-def chat_interface(prompt):
-    # Tokenize input prompt and generate response
-    inputs = tokenizer(prompt, return_tensors="pt", max_length=256, truncation=True)
+def chat_interface(user_input, assistant_input):
+    # Concatenate the user and assistant inputs to simulate a chat conversation
+    chat_history = f"{assistant_input} User: {user_input}"
+
+    # Tokenize the chat history and generate the response
+    inputs = tokenizer(chat_history, return_tensors="pt", max_length=256, truncation=True)
     outputs = model.generate(**inputs)
     response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
     
-    return response
+    return response, chat_history
 
 # Define Gradio Chat Interface
 iface = gr.ChatInterface(
     chat_model=chat_interface,
     title="GPT-2 Chat Interface",
-    inputs=["text"],
-    outputs=["text"],
-    examples = [
-    ["मैं अपने समय प्रबंधन कौशल को कैसे सुधार सकता हूँ? मुझे पांच बिंदु बताएं।"],
-    ["मैं अपने समय प्रबंधन कौशल को कैसे सुधार सकता हूँ? मुझे पांच बिंदु बताएं और उनका वर्णन करें।"],
-    ],
+    inputs=["text", "text"],
+    outputs=["text", "text"],
 )
 
 # Launch Gradio Chat Interface
 iface.launch()
 
 
+
 # import torch
 # from transformers import AutoTokenizer, AutoModelForCausalLM
 # import gradio as gr