app.py

from fastapi import FastAPI
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
from langchain_ollama import OllamaLLM

app = FastAPI()

# Allow requests from your front-end's origin.
app.add_middleware(
    CORSMiddleware,
    allow_origins=["chrome-extension://*"],  # Allow Chrome extensions
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Define the request model that expects a JSON body with "text"
class MeaningRequest(BaseModel):
    text: str

# Define the response model that will return the meaning
class MeaningResponse(BaseModel):
    meaning: str

def get_meaning_from_llm(text: str) -> str:
    """
    Get meaning of text using Ollama LLM.
    """
    # Create a prompt for your LLM
    prompt = f"Explain the meaning of the following text in simple terms in only one or two lines not more than that: '{text}'"
    
    # Make sure this URL is accessible and valid
    llm = OllamaLLM(
        model="llama3.2",
        base_url="https://earwig-exact-slug.ngrok-free.app", 
        temperature=0.25
    )
    meaning = llm(prompt)
    return meaning

@app.post("/get_meaning", response_model=MeaningResponse)
async def get_meaning(request: MeaningRequest):
    """
    Endpoint to receive text and return its 'meaning' as generated by an LLM.
    """
    print(f"Received text: {request.text}")
    # Extract text from the request
    text = request.text
    # Generate meaning using the LLM call
    meaning = get_meaning_from_llm(text)
    # Return the meaning in a JSON response
    return MeaningResponse(meaning=meaning)

if __name__ == "__main__":
    # Run the FastAPI app with Uvicorn
    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)