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Browse files- app.py +63 -0
- requirements.txt +4 -0
app.py
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import streamlit as st
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import os
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from transformers import AutoTokenizer, pipeline
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import torch
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# get the Hugging Face API token from environment variables
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hugging_face_token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
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def getLLamaresponse(input_text, no_words, blog_style):
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model = "daryl149/llama-2-7b-chat-hf"
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tokenizer = AutoTokenizer.from_pretrained(model, use_auth_token=hugging_face_token)
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# Configure the model's device (CPU or GPU) based on the environment
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device = 0 if torch.cuda.is_available() else -1
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text_generation_pipeline = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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use_auth_token=hugging_face_token,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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device=device
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)
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# prompt
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prompt = f"Write a blog for a {blog_style} job profile about {input_text} within {no_words} words.\n"
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# generate
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sequences = text_generation_pipeline(
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prompt,
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do_sample=True,
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top_k=10,
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num_return_sequences=1,
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max_length=int(no_words),
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)
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# print and return the text of the first generated sequence
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for seq in sequences:
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print(f"Result: {seq['generated_text']}")
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return seq['generated_text']
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# streamlit
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st.set_page_config(page_title="Generate Blogs",
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page_icon='🤖',
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layout='centered',
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initial_sidebar_state='collapsed')
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st.header("Generate Blogs 🤖")
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input_text = st.text_input("Enter the Blog Topic")
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# creating to more columns for additonal 2 fields
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col1, col2 = st.columns([5, 5])
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with col1:
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no_words = st.text_input('No of Words')
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with col2:
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blog_style = st.selectbox('Writing the blog for',
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('Researchers', 'Data Scientist', 'Common People'), index=0)
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submit = st.button("Generate")
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# Final response
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if submit:
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st.write(getLLamaresponse(input_text, no_words, blog_style))
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requirements.txt
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@@ -0,0 +1,4 @@
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langchain
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streamlit
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transformers
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torch
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