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import os
import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from huggingface_hub import login
# Automatically use the environment variable
token = os.getenv("Fela")
if token:
login(token)
else:
raise ValueError("Fela environment variable is not set.")
login(os.getenv("Fela"))
# Sidebar
with st.sidebar:
st.markdown("## About Fela")
st.markdown("This chatbot uses the Llm model to answer questions based on the content of an uploaded article. You can upload a text or markdown file and ask questions about its content.")
st.markdown("[View the source code](https://github.com/Al0ni/LLM-Files-QA/blob/main/app.py)")
st.markdown("[](https://codespaces.new/Al04ni/LLM-Files-QA?quickstart=1)")
# Main interface
st.title(" File Q&A 📝 with Fela")
# Initialize variables for model and tokenizer
model_name = "meta-llama/Llama-2-7b-chat-hf"
tokenizer, model = None, None
try:
# Attempt to load the tokenizer and model
with st.spinner("Loading model and tokenizer..."):
tokenizer = AutoTokenizer.from_pretrained(model_name, token=True, cache_dir="./cache")
model = AutoModelForCausalLM.from_pretrained(model_name, token=True, cache_dir="./cache")
st.success("Model and tokenizer loaded successfully!")
except Exception as e:
st.error("Failed to load the model or tokenizer. Please check your configuration.")
st.warning(f"Error Details: {e}")
st.stop() # Stop execution if model loading fails
# Add tabs for suggested questions, easing kabisa
tabs = st.tabs(["Summary", "Key Points", "Actionable Insights", "Critical Analysis", "General"])
#questions suggested for each tab
suggested_questions = {
"Summary": ["Can you summarize the article?", "What are the main points discussed?"],
"Key Points": ["What are the key takeaways?", "What facts are highlighted in the article?"],
"Actionable Insights": ["What actions can be taken based on this article?", "How can the information be applied?"],
"Critical Analysis": ["Are there any biases present?"],
"General": ["Can you rewrite the article?", "Who is the intended audience for this article?"]
}
# Display questions in tabs
selected_question = ""
for tab_name, questions in suggested_questions.items():
with tabs[list(suggested_questions.keys()).index(tab_name)]:
st.markdown(f"### Suggested Questions: {tab_name}")
for question in questions:
if st.button(question, key=f"{tab_name}_{question}"):
selected_question = question
# File uploader and question input
uploaded_file = st.file_uploader("Upload an article", type=("txt", "md"))
question = st.text_input(
"Ask something about the article",
value=selected_question, # Populate the selected question
placeholder="Can you give me a short summary?",
disabled=not uploaded_file,
)
if uploaded_file and question:
article = uploaded_file.read().decode()
prompt = f"Here's an article:\n\n<article>\n{article}\n</article>\n\n{question}"
try:
# Tokenize the input
inputs = tokenizer(prompt, return_tensors="pt")
# Generate the response
with torch.no_grad():
with st.spinner("Generating response..."):
outputs = model.generate(inputs.input_ids, max_length=150)
# Decode the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.write("### Answer")
st.write(response)
except Exception as e:
st.error("An error occurred while processing your query.")
st.warning(f"Error Details: {e}")
else:
if not uploaded_file:
st.info("Please upload a file to get started.")
elif not question:
st.info("Please enter a question to ask about the uploaded file.")
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