Create app.py

app.py

@@ -0,0 +1,91 @@
+import chainlit as cl
+from pydantic import BaseModel, Field
+from dotenv import load_dotenv
+import instructor
+from openai import OpenAI
+import os
+
+load_dotenv()
+
+# Patch the OpenAI client with Instructor
+client = instructor.from_openai(OpenAI(api_key=os.environ['OPENAI_API_KEY']))
+
+# Define the Pydantic models
+class UserProposal(BaseModel):
+    proposal: str = Field(description="This is the proposal of the original user prompt. It should be a clear concise detailed plan to use simple ai software tools to solve specific problem.")
+    is_clear: str = Field(description="Is the proposed plan clear? It specifies which tools it needs to use and how. It lays out each component and how they all connect.")
+    is_detailed: str = Field(description="Is the proposed plan detailed? Each component should have a description of what it does.")
+    is_explicit: str = Field(description="Is the proposed plan explicit? Each component should have a data model to describe their input and output schema.")
+
+class ProposedArchitecture(BaseModel):
+    proposed_architecture: str = Field(description="A detailed AI application architecture with all the tools required for the plan proposed. (e.g. Python packages)")
+
+class PropositionWithRevision(BaseModel):
+    revised_proposed_architecture: str = Field(description="Step by step implementation of software solution.")
+
+# Define functions
+def extract_user_proposal_details(user_proposal: str) -> UserProposal:
+    return client.chat.completions.create(
+        model="gpt-4-turbo-preview",
+        response_model=UserProposal,
+        messages=[
+            {"role": "user", "content": user_proposal},
+        ],
+    )
+
+def generate_proposed_architecture(proposal: str) -> ProposedArchitecture:
+    return client.chat.completions.create(
+        model="gpt-4-turbo-preview",
+        response_model=ProposedArchitecture,
+        messages=[
+            {"role": "user", "content": f"Write a detailed AI application architecture with all the tools required for the plan proposed: \n\n{proposal}"},
+        ],
+    )
+
+def revise_architecture(proposed_architecture: str) -> PropositionWithRevision:
+    return client.chat.completions.create(
+        model="gpt-4-turbo-preview",
+        response_model=PropositionWithRevision,
+        messages=[
+            {"role": "user", "content": f"Revise the plan proposed: \n\n{proposed_architecture}\n\nThe plan should be a step by step implementation of software solution."},
+        ],
+    )
+
+# Define the Chainlit message handler
+@cl.on_message
+async def main(message: cl.Message):
+    user_proposal = message.content
+
+    user_proposal_details = extract_user_proposal_details(user_proposal)
+    
+    proposed_architecture = generate_proposed_architecture(user_proposal_details.proposal)
+    
+    await cl.Message(
+        content=f"Proposed Architecture:\n{proposed_architecture.proposed_architecture}"
+    ).send()
+
+    feedback_message = await cl.AskUserMessage(content="What do you think about this proposed plan and alleged architecture?", timeout=300).send()
+    if feedback_message:
+        human_feedback_of_proposed_plan = feedback_message['output']
+    
+        revised_architecture = revise_architecture(proposed_architecture.proposed_architecture)
+        
+        await cl.Message(
+            content=f"Revised Architecture:\n{revised_architecture.revised_proposed_architecture}"
+        ).send()
+
+        with open("output.md", "w") as output_file:
+            output_file.write("# User Proposal\n")
+            output_file.write(user_proposal_details.proposal + "\n\n")
+            output_file.write("# Proposed Architecture\n")
+            output_file.write(proposed_architecture.proposed_architecture + "\n\n")
+            output_file.write("# Revised Architecture\n")
+            output_file.write(revised_architecture.revised_proposed_architecture + "\n")
+
+        await cl.Message(
+            content="The results have been saved to output.md"
+        ).send()
+    else:
+        await cl.Message(
+            content="No feedback received. Exiting."
+        ).send()