diff --git a/app.py b/app.py
new file mode 100644
index 00000000..dcbaf3fc
--- /dev/null
+++ b/app.py
@@ -0,0 +1,126 @@
+import streamlit as st
+import openai
+import json
+import requests
+from policyengine_uk import Simulation
+
+def print_inputs_outputs(fn):
+    return fn
+    def wrapper(*args, **kwargs):
+        st.write(f"{fn.__name__} inputs: {args}, {kwargs}")
+        result = fn(*args, **kwargs)
+        st.write(f"{fn.__name__} outputs: {result}")
+        return result
+    return wrapper
+
+
+class GPTAgent:
+    gpt_model = "gpt-4"
+
+    capabilities_description: str = ""
+
+    def process(self, text: str) -> str:
+        raise NotImplementedError("Must implement process method")
+    
+    def __str__(self):
+        return self.__class__.__name__
+    
+    def _get_chatgpt_response(self, text: str) -> str:
+        response = openai.ChatCompletion.create(
+            model=self.gpt_model,
+            messages=[{
+                "role": "user",
+                "content": text
+            }]
+        )
+        return response.choices[0].message.content
+
+
+metadata = requests.get("https://api.policyengine.org/uk/metadata").json()["result"]
+
+class InputHouseholdConstructor(GPTAgent):
+    gpt_model = "gpt-3.5-turbo"
+
+    capabilities_description = "Constructs a household object given a description"
+
+    @print_inputs_outputs
+    def process(self, text: str) -> str:
+        example_household = {
+            "people": {
+                "person_1": {
+                    "age": {"2023": 30},
+                    "employment_income": {"2023": 30000},
+                },
+            },
+            "households": {
+                "household": {
+                    "members": ["person_1"],
+                },
+            },
+        }
+        PROMPT = f"""You need to construct a PolicyEngine-compatible household JSON object from the user's description. Here's a valid example:
+        {example_household}
+        Description: {text}
+        Remember JSON must use double quotes etc.
+        JSON result:
+        """
+        return self._get_chatgpt_response(PROMPT)
+
+class VariableCalculator(GPTAgent):
+    gpt_model = None
+
+    capabilities_description = "Calculates net income given a (stringified) JSON object {{household: <household object>, variable_name: <variable_name>}}"
+
+    @print_inputs_outputs
+    def process(self, text: str) -> str:
+        input_data = json.loads(text)
+        sim = Simulation(situation=input_data["household"])
+        return str(sim.calculate(input_data["variable_name"]).sum())
+    
+class NormalGPTAgent(GPTAgent):
+    gpt_model = "gpt-3.5-turbo"
+    capabilities_description = "Just sends the text to GPT-4 and returns the response."
+
+    @print_inputs_outputs
+    def process(self, text: str) -> str:
+        return self._get_chatgpt_response(text + "do not cop out: all the data you need is given.")
+    
+agents = [NormalGPTAgent(), InputHouseholdConstructor(), VariableCalculator()]
+
+class GPTAgentManager(GPTAgent):
+    gpt_model = "gpt-4"
+    capabilities_description = "Receives a question and formulates a Python script that can answer it, given information about the capabilities of GPT-powered agents it can interact with."
+
+    def process(self, text: str) -> dict:
+        PROMPT = f"""
+            Task: answer the following question with a Python code snippet that can be run to answer the question.
+            You have several Python GPTAgent classes you can use. Each GPTAgent has the method process(text) that takes a string and returns a string. Each GPTAgent also has a capabilities_description attribute that describes what it can do. Here is a list of GPTAgents you can use:
+            {[dict(name=agent.__class__.__name__, description=agent.capabilities_description) for agent in agents]}
+            You need to write a Python script, specifying which GPTAgent to use and what text (or the output of which agent) to pass to the process method. 
+            All agents receive and return strings.
+            Here is an example:
+            'what's my net income at 40k earnings?'
+            answer:
+            household = InputHouseholdConstructor().process("I am 30 years old and earn 40k")
+            net_income = VariableCalculator().process(json.dumps(dict(household=json.loads(household), variable_name="net_income")))
+            answer = NormalGPTAgent().process(f"summarise to the user that their net income is {{net_income}}, with appropriate formatting (don't cop out and give me rubbish)")
+            
+            Here is the user's question, respond with valid Python text that can be run through exec (must store the answer in a variable called answer):"""
+        python_code = self._get_chatgpt_response(PROMPT + text)
+        # st.write(f"```python\n{python_code}```")
+        local_variables = {
+            "InputHouseholdConstructor": InputHouseholdConstructor,
+            "VariableCalculator": VariableCalculator,
+            "NormalGPTAgent": NormalGPTAgent,
+            "json": json,
+        }
+        exec(python_code, {}, local_variables)
+        answer = local_variables["answer"]
+        return answer
+
+text = st.text_input("Enter text")
+submit = st.button("Submit")
+answer = None
+if submit:
+    answer_question = GPTAgentManager().process(text)
+    st.write(answer_question)
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