Update app.py

app.py

@@ -1,20 +1,28 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from fastapi.middleware.cors import CORSMiddleware
 import uvicorn
-from langchain_ollama import OllamaLLM
+from langchain_google_genai import ChatGoogleGenerativeAI
+import os
+from dotenv import load_dotenv
+
+load_dotenv()  
 
 app = FastAPI()
 
+
 # Allow requests from your front-end's origin.
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["chrome-extension://*"],  # Allow Chrome extensions
+    allow_origins=["http://localhost:3000", "chrome-extension://*"],  # Allow specific origins
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
+# It's recommended to load secrets from environment variables
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
 # Define the request model that expects a JSON body with "text"
 class MeaningRequest(BaseModel):
     text: str
@@ -25,33 +33,42 @@ class MeaningResponse(BaseModel):
 
 def get_meaning_from_llm(text: str) -> str:
     """
-    Get meaning of text using Ollama LLM.
+    Get meaning of text using Google's Generative AI.
     """
     # 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
+    llm = ChatGoogleGenerativeAI(
+        model="gemini-1.5-flash",
+        temperature=0.1,
+        max_tokens=None,
+        timeout=None,
+        max_retries=2,
+        google_api_key=GOOGLE_API_KEY
     )
-    meaning = llm(prompt)
-    return meaning
+    response = llm.invoke(prompt)
+    return response.content
 
 @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.
+    Endpoint to return meaning.
     """
-    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)
+    try:
+        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 MeaningResponse(
+            meaning=meaning
+        )
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
 
 if __name__ == "__main__":
     # Run the FastAPI app with Uvicorn
-    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)
+    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)