updated app file

app.py

@@ -0,0 +1,36 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")
+model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")
+
+def predict(input,knowledge, history=[]):
+#     instruction="Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge."
+    instruction="Instruction: given a dialog context, you need to response empathically"
+    knowledge = '[KNOWLEDGE]' + knowledge
+    s = list(sum(history, ()))
+    s.append(input)
+    dialog = ' EOS ' .join(s)
+    query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
+    top_p = 0.9
+    min_length = 8
+    max_length = 64
+    new_user_input_ids = tokenizer.encode(f"{query}", return_tensors='pt')
+    print(input,s)
+    output = model.generate(new_user_input_ids, min_length=int(
+        min_length), max_length=int(max_length), top_p=top_p, do_sample=True).tolist()
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    history.append((input, response))
+    return history, history
+
+gr.Interface(fn=predict,
+             inputs=["text","text",'state'],
+            
+             outputs=["chatbot",'state']).launch()
+