Update app.py

app.py

@@ -1,6 +1,22 @@
 import streamlit as st
-from main import generate_blog
+from langchain.chains import LLMChain
+from langchain_core.prompts import PromptTemplate
+from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline,BitsAndBytesConfig
+quants = BitsAndBytesConfig(load_in_4bit=True)
 
+template = ''' You are an expert Blog generator , Given the Topic , the intended audience and the maximum number of words ,
+    Write a blog on the given topic
+
+    Topic : {topic}
+    Intended Audince : {role}
+    Number of Words : {words}
+
+    Strictly return the output in a markdown format.
+    Return only the blog and do not provide any other information.'''
+
+
+prompt = PromptTemplate(template = template,input_variables = ['topic','role','words'])
 
 
 def main():
@@ -23,8 +39,14 @@ def main():
     word_count = st.sidebar.slider("Number of Words", min_value=50, max_value=1000, value=200, step=50)
 
     if st.sidebar.button("Generate Blog"):
-        blog_content = generate_blog(role, word_count ,topic)
-        st.markdown(blog_content, unsafe_allow_html=True)
+        model_id = "mistralai/Mistral-7B-Instruct-v0.2"
+        tokenizer = AutoTokenizer.from_pretrained(model_id,quantization_config=quants)
+        model = AutoModelForCausalLM.from_pretrained(model_id,quantization_config=quants)
+        pipe = pipeline("text-generation", model=model, tokenizer=tokenizer,max_new_tokens=1000)
+        hf = HuggingFacePipeline(pipeline=pipe)
+        chain = LLMChain(llm=hf,prompt=prompt,verbose=True)
+        aa = chain.invoke({"topic": topic,"words":word_count,"role":role})
+        st.write(aa)
 
 if __name__ == "__main__":
     main()