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This project focuses on leveraging advanced language models, GPT-4o and GPT-4o-mini, to generate concise, personalized invitation texts tailored for various professional events. The system takes user-provided prompts and individual details such as Name, Job Title, Organisation, Area of Interest, and Category to produce brochure-suitable, user defines-word count in invitation text.

.env

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+GROQ_API_KEY=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
+OPENAI_API_KEY=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

app.py

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+import streamlit as st
+import pandas as pd
+from openai_llms import LLMHandler
+import tempfile
+import os
+
+# Load environment variables for OpenAI API Key
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Initialize LLMHandler
+llm_handler = LLMHandler()
+
+
+def process_csv(file, user_prompt):
+    """Read CSV, generate responses using LLMHandler, and return processed DataFrame."""
+    df = pd.read_csv(file)
+    responses = []
+
+    for _, row in df.iterrows():
+        try:
+            response = llm_handler.generate_response(user_prompt, row.to_dict())
+            responses.append(response)
+        except Exception as e:
+            responses.append(f"Error: {e}")
+
+    df["Generated Text"] = responses
+    return df
+
+
+# Streamlit UI
+st.title("Personalized Invitation Generator")
+
+# File uploader for CSV
+uploaded_file = st.file_uploader("Upload CSV File", type=["csv"])
+user_prompt = st.text_area("Enter the prompt for generating invitation texts:", "Write professional invitation text...")
+
+if uploaded_file is not None and user_prompt:
+    st.write("Processing file...")
+    processed_df = process_csv(uploaded_file, user_prompt)
+
+    # Display results
+    st.dataframe(processed_df)
+
+    # Option to download the processed CSV
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as temp_file:
+        processed_df.to_csv(temp_file.name, index=False)
+        temp_file.close()  # Ensure the file is properly closed before proceeding
+
+        st.download_button(
+            label="Download Results CSV",
+            data=open(temp_file.name, "rb"),
+            file_name="generated_invitations.csv",
+            mime="text/csv"
+        )
+
+    # Now safely delete the temporary file
+    os.unlink(temp_file.name)

openai_llms.py

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+from openai import OpenAI
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+
+class LLMHandler:
+    def __init__(self, model_name="gpt-4o-mini"):
+        """
+        Initializes the LLMHandler with the specified OpenAI model.
+        """
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        if not self.openai_api_key:
+            raise ValueError("OPENAI_API_KEY environment variable not set.")
+
+        # Initialize OpenAI client
+        self.client = OpenAI(api_key=self.openai_api_key)
+        self.model_name = model_name
+
+    def generate_response(self, user_prompt, data):
+        """
+        Generate a concise response using the LLM based on user prompt and data.
+        :param user_prompt: Prompt provided by the user.
+        :param data: Dictionary containing the instance information.
+        :return: Generated response text.
+        """
+        # Refined prompt to handle encoding and formatting
+        prompt = (
+            f"You are a professional AI model tasked with writing personalized invite texts "
+            f"that are concise (less than 40 words), brochure-suitable, and tailored as per the category in the given sample.\n\n"
+            f"Consider the user prompt: {user_prompt}\n\n"
+            f"Details of the individual:\n"
+            f"- Name: {data['Name']}\n"
+            f"- Job Title: {data['Job Title']}\n"
+            f"- Organisation: {data['Organisation']}\n"
+            f"- Area of Interest: {data['Area of Interest']}\n"
+            f"- Category: {data['Category']}\n\n"
+            f"The response **MUST**:\n"
+            f"- Start with 'Hello {data['Name']}'.\n"
+            f"- Be concise, professional, and STRICTLY DO NOT generate invalid characters or encoding errors (e.g. 'SoraVR’s').\n"
+            f"- Use standard English punctuation, such as single quotes (e.g., 'can't', 'it's').\n"
+            f"- STRICTLY Give only one response for the Category the sample belongs to.\n"
+            f"- Do NOT include preambles or unnecessary text.\n\n"
+            f"Return the final response cleanly, without any extraneous symbols or characters."
+        )
+
+        # Query the OpenAI client and return the response
+        completion = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=[
+                {"role": "system", "content": "You are a professional assistant."},
+                {"role": "user", "content": prompt},
+            ]
+        )
+
+        # Extract and clean the generated response
+        response = completion.choices[0].message.content.strip()
+
+        # Optional: Post-process to clean invalid characters
+        #response_cleaned = response.encode('utf-8').decode('utf-8', errors='ignore')
+
+        return response

requirements.txt

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+numpy~=1.26.4
+pandas~=2.2.3
+sentence-transformers~=3.2.0
+python-dotenv~=1.0.1
+langchain-openai
+langchain_groq
+langchain
+langchain_community
+openai
+langchain-community~=0.3.3
+langchain-core~=0.3.12
+streamlit