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import streamlit as st | |
from langchain import PromptTemplate | |
from langchain.chains import LLMChain | |
# Import your model and template | |
llm_hf = HuggingFaceHub( | |
repo_id="tiiuae/falcon-7b-instruct", | |
model_kwargs={"temperature": 0.3} | |
) | |
restaurant_template = """ | |
I want you to become an indian lawyer and enlist me the acts and rights with a one liner description, for every issue i'll raise, you've to only give me relevant acts and rights covering from that issue... | |
""" | |
# Define the Streamlit app | |
def main(): | |
st.title("Indian Legal Consultation") | |
# Input for the user's issue | |
description = st.text_area("Please describe your issue:") | |
if st.button("Get Legal Advice"): | |
if description: | |
# Create a PromptTemplate | |
prompt = PromptTemplate( | |
input_variables=["issue"], | |
template=restaurant_template, | |
) | |
# Create an LLMChain | |
chain = LLMChain(llm=llm_hf, prompt=prompt) | |
# Generate a response | |
response = chain.run(description) | |
# Display the response | |
st.subheader("Legal Advice:") | |
st.write(response) | |
else: | |
st.warning("Please provide a description of your issue.") | |
if __name__ == "__main__": | |
main() | |