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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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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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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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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",google_api_key =st.secrets["YOUR_GEMINI_API_KEY"]) |
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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() |
