Update app.py

app.py

@@ -1,8 +1,32 @@
 import streamlit as st 
-from transformers import pipeline 
-classifier = pipeline("sentiment-analysis") 
-text = st.text_area("Enter your text") 
+from datasets import load_dataset 
+from transformers import AutoModelForSeq2SeqLM 
+from transformers import AutoTokenizer 
+from transformers import GenerationConfig 
+
+huggingface_dataset_name = "dshihk/llm-generated-essay" 
+dataset = load_dataset(huggingface_dataset_name) 
+
+model_name = 'google/flan-t5-base' 
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name) 
+
+
+# get the topic
+topic = st.text_enter("Enter your desired Topic of Blog")
+
+if topic:
+    # Configurations
+    # generation_config = GenerationConfig(max_new_tokens=50, do_sample=True, temperature=0.7)
+    generation_config = GenerationConfig(max_new_tokens=50)
+    
+    # Encode input:
+    inputs_encoded = tokenizer(topic, return_tensors='pt')
+    
+    # Model Output:
+    model_output = model.generate(inputs_encoded["input_ids"], generation_config=generation_config)[0]
+    
+    # Decode the output
+    zero_output = tokenizer.decode(model_output, skip_special_tokens=True)
+
+    st.json(zero_output)
 
-if text : 
-    out = classifier(text) 
-    st.json(out)