afeat: ddd requirements.txt + model to app

app.py

@@ -1,13 +1,32 @@
 import streamlit as st
+from transformers import AutoTokenizer, TFAutoModelForCausalLM
+
+# MODEL TO CALL
+
+model_name = "Alirani/distilgpt2-finetuned-synopsis"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = TFAutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_synopsis(model, tokenizer, title):
+    input_ids = tokenizer(title, return_tensors="tf")
+    output = model.generate(input_ids['input_ids'], max_length=150, num_beams=5, no_repeat_ngram_size=2, top_k=50, attention_mask=input_ids['attention_mask'])
+    synopsis = tokenizer.decode(output[0], skip_special_tokens=True)
+    return synopsis
 
 favicon = "https://i.ibb.co/JRdhFZg/favicon-32x32.png"
 
-st.set_page_config(page_title='Chatbot', page_icon = favicon, layout = 'wide', initial_sidebar_state = 'auto')
+st.set_page_config(page_title="LoreFinder-demo", page_icon = favicon, layout = 'wide', initial_sidebar_state = 'auto')
 
 st.title('Demo LoreFinder')
 
 st.header('Generate a story')
 
-st.text_input('Type a title to generate a story')
+prod_title = st.text_input('Type a title to generate a synopsis')
+
+st.button('Get synopsis')
+
+if prod_title:
+    gen_synopsis = generate_synopsis(model, tokenizer, prod_title)
+    st.text_area(gen_synopsis, disabled=True)
+
 
-st.button('Get synopsis')

requirements.txt

@@ -0,0 +1 @@
+transformers