Update app.py

app.py

@@ -1,29 +1,16 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from langchain.chains import LLMChain
+from langchain.llms import HuggingFaceHub
+from langchain.prompts import PromptTemplate
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelWithLMHead  
-import torch
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-else:
-    device = "cpu"
-    
-    
-tokenizer = AutoTokenizer.from_pretrained("salesken/content_generation_from_phrases") 
-model = AutoModelWithLMHead.from_pretrained("salesken/content_generation_from_phrases").to(device)
-
-
-input_query=st.text_input("Enter the Blog Title")
-query = "<|startoftext|> " +"Create a blog about "+ input_query + " ~~"
-
-input_ids = tokenizer.encode(query.lower(), return_tensors='pt').to(device)
-sample_outputs = model.generate(input_ids,
-                                do_sample=True,
-                                num_beams=1, 
-                                max_length=4096,
-                                temperature=0.9,
-                                top_k = 30,
-                                num_return_sequences=1)
-r = tokenizer.decode(sample_outputs[0], skip_special_tokens=True).split('||')[0]
-r = r.split(' ~~ ')[1]
-
-st.write(r)
+import os
+topic = st.text_input("Enter Topic for the bog")
+hub_llm = HuggingFaceHub(repo_id ="HuggingFaceH4/zephyr-7b-beta")
+prompt = PromptTemplate(
+    input_variables = ['keyword'],
+    template = """
+    Write a comprehensive article about {keyword} covering the following aspects:
+    Introduction, History and Background, Key Concepts and Terminology, Use Cases and Applications, Benefits and Drawbacks, Future Outlook, Conclusion
+    Ensure that the article is well-structured, informative, and at least 1500 words long. Use SEO best practices for content optimization.
+    """)
+hub_chain = LLMChain(prompt=prompt,llm = hub_llm,verbose=True)
+st.write(hub_chain.run(topic))