diff --git a/main.py b/main.py
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index 0000000..8cc22a5
--- /dev/null
+++ b/main.py
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+import os
+import sys
+from langchain_openai import ChatOpenAI
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+from models import PersonInfo, MeetingNotes
+
+# Choose LLM
+llm = ChatOpenAI(temperature=0)
+
+# Prepare parser for each model
+person_parser = PydanticOutputParser(pydantic_object=PersonInfo)
+meeting_parser = PydanticOutputParser(pydantic_object=MeetingNotes)
+
+# Prompt templates
+person_prompt = PromptTemplate(
+    input_variables=["text"],
+    template="""
+    Extract structured data about a person from the following text.
+    The output must follow the JSON schema:
+    {schema}
+    Text: {text}
+    """,
+    partial_variables={"schema": person_parser.get_format_instructions()},
+)
+
+meeting_prompt = PromptTemplate(
+    input_variables=["text"],
+    template="""
+    Extract structured data about a meeting from the following text.
+    The output must follow the JSON schema:
+    {schema}
+    Text: {text}
+    """,
+    partial_variables={"schema": meeting_parser.get_format_instructions()},
+)
+
+# Helper to detect type
+
+def detect_type(text: str) -> str:
+    # Simple heuristic: if contains "meeting" or "participants" -> meeting, else person
+    lower = text.lower()
+    if "meeting" in lower or "participants" in lower or "topics" in lower:
+        return "meeting"
+    return "person"
+
+def main():
+    if len(sys.argv) > 1:
+        input_text = " ".join(sys.argv[1:])
+    else:
+        print("Enter text (or press Ctrl-D to exit):")
+        input_text = sys.stdin.read().strip()
+        if not input_text:
+            print("No input provided.")
+            return
+
+    t = detect_type(input_text)
+    if t == "person":
+        chain = person_prompt | llm | person_parser
+        result = chain.invoke({"text": input_text})
+        print("\nParsed PersonInfo:\n", result.model_dump(indent=2))
+    else:
+        chain = meeting_prompt | llm | meeting_parser
+        result = chain.invoke({"text": input_text})
+        print("\nParsed MeetingNotes:\n", result.model_dump(indent=2))
+
+if __name__ == "__main__":
+    main()
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