Update app.py

app.py

@@ -19,15 +19,16 @@ known_questions_answers = {
     "Do you speak English?": "I can understand and respond to English questions.",
 }
 
-def predict(input, history=[]):
+def predict(input_text, chatbot_state):
     response = None
+    history = chatbot_state
     
     # Check if the input question is in the known question-answer pairs
-    if input in known_questions_answers:
-        response = known_questions_answers[input]
+    if input_text in known_questions_answers:
+        response = known_questions_answers[input_text]
     else:
         # Tokenize the new user input sentence
-        new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")
+        new_user_input_ids = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors="pt")
         # Append the new user input tokens to the chat history
         bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
         # Generate a response
@@ -37,19 +38,22 @@ def predict(input, history=[]):
         # Convert tokens to text, and split the response into lines
         response = tokenizer.decode(history[0], skip_special_tokens=True)
     
-    print("Response:", response)  # Debug statement
-    return response
+    return response, history
 
 def main():
     # You can add logic here to read known question-answer pairs, for example, from a JSON file
     pass
 
+textbox_output = gr.outputs.Textbox(label="Chatbot Response")
+state_input = "text"
+state_output = "state"
+
 gr.Interface(
     fn=predict,
     title=title,
     description=description,
     examples=examples,
-    inputs=["text", "state"],
-    outputs="text",
+    inputs=["text", state_input],
+    outputs=[textbox_output, state_output],
     theme="finlaymacklon/boxy_violet",
 ).launch()