Create app4.py
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app4.py
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import streamlit as st
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from langchain_community.llms import OpenAI
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from langchain_google_genai import ChatGoogleGenerativeAI
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def main():
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st.title("Open AI or Gemini Options")
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# Radio
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st.header("Radio:")
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radio = st.radio("Radio", ["Open AI", "Gemini", "TBD"])
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st.write("Selected option:", radio)
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role = st.text_input("Enter Role")
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st.write("Entered role:", role)
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# Slider
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st.header("Slider:")
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temp = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.1)
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st.write("Selected value:", temp)
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# Topic
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with st.form("my_form"):
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topic = st.text_area("Enter the topic for your LinkedIn post:")
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submitted = st.form_submit_button("Generate Post")
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if submitted and topic:
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post = generate_linkedin_post(topic, role, temp, radio)
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st.info(post)
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elif submitted and not topic:
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st.error("Please enter a topic to generate a post.")
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def generate_linkedin_post(topic, role, temp, radio):
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prompt = (
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f"You as {role} Create a professional, engaging LinkedIn post about {topic}. "
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f"Adjust the tone and style based on a temperature of {temp}. "
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"It should start with an attention grabbing hook based on audience pain. "
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"Then a line to agitate the user. This should be in the next line. "
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"The post should be concise, informative, and suitable for a professional audience. "
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"It should provide value, insights, or thought-provoking content related to the topic. "
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"And only contain 3 points."
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)
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if radio == "Open AI":
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llm = OpenAI(temperature=temp, openai_api_key=st.secrets["OPENAI_API_KEY"])
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response = llm(prompt)
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return response
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elif radio == "Gemini":
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llm = ChatGoogleGenerativeAI(model="gemini-pro")
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result = llm.invoke(prompt)
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return result.content
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if __name__ == "__main__":
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main()
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