Upload 4 files

app (2).py

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+import streamlit as st
+#from langchain.llms import OpenAI
+from langchain_community.llms import OpenAI
+
+
+st.title("🔗 LinkedIn Post Generator")
+
+def generate_linkedin_post(topic):
+    # Enhanced prompt with additional context for better post generation
+    prompt = (
+        f"Create a professional, engaging LinkedIn post about {topic}. "
+        "It should start with a attention grabbing hook based on audience pain"
+        "Then a line to agitate the user. This should be in the next line"
+        "The post should be concise, informative, and suitable for a professional audience. "
+        "It should provide value, insights, or thought-provoking content related to the topic. "
+        "And only contain 3 points."
+        "The tone should be positive and encouraging, suitable for networking and professional growth."
+    )
+    llm = OpenAI(temperature=0.7, openai_api_key=st.secrets["OPENAI_API_KEY"])
+    response = llm(prompt)
+    return response
+
+with st.form("my_form"):
+    topic = st.text_area("Enter the topic for your LinkedIn post:")
+    submitted = st.form_submit_button("Generate Post")
+    if submitted and topic:
+        post = generate_linkedin_post(topic)
+        st.info(post)
+    elif submitted and not topic:
+        st.error("Please enter a topic to generate a post.")

app3.py

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+#from langchain.llms import OpenAI
+import streamlit as st
+from langchain_community.llms import OpenAI
+
+
+st.title("🔗 LinkedIn  Test Post Generator two options")
+
+def generate_linkedin_post(topic):
+    # Enhanced prompt with additional context for better post generation
+    prompt = (
+       f"Create a LinkedIn post about {topic} that grabs attention and makes people chuckle. Here's how:" +
+        f"Start with a relatable pain point:** Something the audience groans about inwardly (e.g., 'Ever spend hours on a task that should take minutes?')"+
+        f"Agitate with a pinch of absurdity:** Exaggerate the pain point in a slightly ridiculous way (e.g., '...only to discover your cat was sitting on the keyboard the whole time.')"+
+        f"***Offer a solution (with a twist):** Provide a real tip but phrase it unexpectedly (e.g., 'Turns out, the answer lies in [insightful tip] - and checking for rogue felines.')"+
+        f"***Tone:** Confident, slightly tongue-in-cheek. Aim for a knowing smile, not a belly laugh.") 
+    llm = OpenAI(temperature=0.7, openai_api_key=st.secrets["OPENAI_API_KEY"])
+    response = llm(prompt)
+    return response
+
+with st.form("my_form"):
+    topic = st.text_area("Enter the topic for your LinkedIn post:")
+    submitted = st.form_submit_button("Generate Post")
+    if submitted and topic:
+        post = generate_linkedin_post(topic)
+        st.info(post)
+    elif submitted and not topic:
+        st.error("Please enter a topic to generate a post.")
+
+with st.form("my_form1"):
+    topic2 = st.text_area("Enter the topic 2 for your LinkedIn post:")
+    submitted2 = st.form_submit_button("Generate Post")
+    if submitted2 and topic2:
+        post = generate_linkedin_post(topic)
+        st.info(post)
+    elif submitted2 and not topic2:
+        st.error("Please enter a topic 2 to generate a post.")

app4.py

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+import streamlit as st
+from langchain_community.llms import OpenAI
+from langchain_google_genai import ChatGoogleGenerativeAI
+st.set_page_config(layout="wide")
+
+
+# Function to handle AI invocation and response
+def analyze_job_description(topic, model ):
+    prompt = (
+        f"As an HR Manager, I need you to analyze the following job description and identify the key technical skills, non technical skills or soft skills , further divide it nice-to-have skills, must-have skills,  required for the role: {topic}. "
+        "The post should be concise, informative, and suitable for a professional audience. "
+        "List top 5 points for technical skills, nice-to-have skills, must-have skills, and soft skills required for the role."
+    )
+        
+    if model == "Open AI":
+        # llm = OpenAI(openai_api_key=st.secrets["OPENAI_API_KEY"])
+        response ="Whoops! Looks like someone's got champagne tastes on a lemonade budget. How about we explore those other options for now? 😉"
+        return response
+    elif model == "Gemini":
+        llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"])
+        result = llm.invoke(prompt)
+        return result.content
+
+def main():
+    st.title("JD Analysis")
+    # Radio selection for AI model
+    st.header("Select AI:")
+    model = st.radio("Model", [ "Gemini","Open AI",])
+    st.write("Selected option:", model)
+    
+    # Text area for job description input
+    with st.form("my_form"):
+        topic = st.text_area("Copy Paste the JD here:")
+        submitted = st.form_submit_button("Analyze Now")
+        if submitted and topic:
+            result = analyze_job_description(topic, model )
+            st.info(result)
+        elif submitted and not topic:
+            st.error("Please enter a JD details to analyze.")
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+langchain_community
+requests
+openai
+langchain_google_genai