Update app.py

app.py

@@ -1,32 +1,32 @@
-try:
+# try:
     
         
-    import streamlit as st
-    from transformers import AutoTokenizer,pipeline
+#     import streamlit as st
+#     from transformers import AutoTokenizer,pipeline
 
-    model_name ="NousResearch/Llama-2-7b-chat-hf"
-    print('tokenizer_loading')
-    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-    tokenizer.pad_token = tokenizer.eos_token
-    tokenizer.padding_side = "right" 
-    print('tokenizer_loaded')
+#     model_name ="NousResearch/Llama-2-7b-chat-hf"
+#     print('tokenizer_loading')
+#     tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+#     tokenizer.pad_token = tokenizer.eos_token
+#     tokenizer.padding_side = "right" 
+#     print('tokenizer_loaded')
     
-    model = "Hardik1234/llama-finetune-reactjs"
-    print('loading_model')
-    pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=2048)
-    print('model_loaded')
-    prompt = st.text_area('Enter prompt: ')
-    if prompt:
-        print('taking prompt')
-        result = pipe(f"<s> [INST] {prompt} [/INST] ")
-        print('generating output')
-        st.json(result[0]['generated_text'])
-except Exception as e:
-    print(e)
+#     model = "Hardik1234/llama-finetune-reactjs"
+#     print('loading_model')
+#     pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=2048)
+#     print('model_loaded')
+#     prompt = st.text_area('Enter prompt: ')
+#     if prompt:
+#         print('taking prompt')
+#         result = pipe(f"<s> [INST] {prompt} [/INST] ")
+#         print('generating output')
+#         st.json(result[0]['generated_text'])
+# except Exception as e:
+#     print(e)
 
-# import streamlit as st
-# from transformers import AutoTokenizer,pipeline
-# pipe = pipeline('sentiment-analysis')
-# text = st.text_area('Enter text:')
-# if text:
-#     st.json(pipe(text))
+import streamlit as st
+from transformers import AutoTokenizer,pipeline
+pipe = pipeline('sentiment-analysis')
+text = st.text_area('Enter text:')
+if text:
+    st.json(pipe(text))