Upload app.py

app.py

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+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain.llms import CTransformers
+from langchain import HuggingFaceHub
+import os
+
+import warnings
+warnings.filterwarnings('ignore')
+#streamlit run app.py 
+def get_llama_response(inputtext,wordcount,blogstyle):
+    ### LLama2 Model Calling..
+    # llm = CTransformers(model="model/llama-2-7b-chat.ggmlv3.q2_K.bin",
+    #                     model_type='llama',
+    #                     config={'max_new_tokens':256,
+    #                     'temperature':0.01})
+    template = f"""
+    Write a Blog for {blogstyle} job profile for a topic {inputtext} within {wordcount} words.
+    """
+    
+    
+    llm_hugg = HuggingFaceHub(repo_id="google/flan-t5-large",model_kwargs={'temperature':0.6, "max_length":64})
+    promptTemp = PromptTemplate(input_variables=['blogstyle','inputtext','wordcount'],
+                                template=template)
+    response = llm_hugg(promptTemp.format(blogstyle=blogstyle,inputtext=inputtext,wordcount=wordcount))
+
+    return response
+
+
+
+st.set_page_config(page_title='Blog Generation',page_icon="🧊",layout="centered",initial_sidebar_state="collapsed")
+
+st.header("Generate Blogs")
+
+input_text = st.text_input("Enter the Blog Topic")
+col1,col2 = st.columns([5,5])
+
+with col1:
+    num_words = st.text_input("No Of Words")
+with col2:
+    blog_style = st.selectbox("Writing the Blog For," ,("Researchers","Data Scientist","Common People"),index=0)
+
+submit =st.button("Generate")
+
+if submit:
+    st.subheader("The Response is:")
+    st.write(get_llama_response(input_text,num_words,blog_style))