Update app.py

app.py

@@ -1,66 +1,66 @@
-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=["*"],
-)
-
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    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("main:app", host="0.0.0.0", port=8000, reload=True)
+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=["*"],
+)
+
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    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)