Update app.py

app.py

@@ -1,7 +1,6 @@
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Depends
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
-from transformers import pipeline
 import difflib
 import spacy
 import re
@@ -9,13 +8,8 @@ from nltk.sentiment import SentimentIntensityAnalyzer
 import nltk
 from collections import Counter
 import uvicorn
-import requests
-from dotenv import load_dotenv
 import os
-
-load_dotenv()
-
-HF_API_TOKEN = os.getenv("HF_API_TOKEN")
+import requests
 
 # Download NLTK resources
 try:
@@ -34,23 +28,39 @@ app.add_middleware(
     allow_credentials=True,
     allow_methods=["*"],  # Allows all methods
     allow_headers=["*"],  # Allows all headers
-) 
+)
 
-humanize_pipe = None  # Default to None before trying to load
+# Function to get API token
+def get_hf_api_token():
+    token = os.getenv("HF_API_TOKEN")
+    if not token:
+        raise HTTPException(status_code=500, detail="Hugging Face API token not configured")
+    return token
 
-try:
-    # humanize_pipe = pipeline("text2text-generation", model="danibor/flan-t5-base-humanizer")
-    print("Humanization model loaded successfully!")
+# Function to call Hugging Face Inference API
+def get_humanized_text(text, token):
+    API_URL = "https://api-inference.huggingface.co/models/danibor/flan-t5-base-humanizer"
+    headers = {"Authorization": f"Bearer {token}"}
     
+    try:
+        response = requests.post(API_URL, headers=headers, json={"inputs": text})
+        response.raise_for_status()  # Raise exception for HTTP errors
+        return response.json()[0]["generated_text"]
+    except Exception as e:
+        print(f"Error calling Hugging Face API: {e}")
+        return "Error processing text with Hugging Face API."
+
+# Load NLP models
+try:
     # Load spaCy model
     nlp = spacy.load("en_core_web_sm")
     
     # Initialize sentiment analyzer
     sentiment_analyzer = SentimentIntensityAnalyzer()
     
-    print("All NLP models loaded successfully!")
+    print("NLP models loaded successfully!")
 except Exception as e:
-    print(f"Error loading humanization model: {e}")
+    print(f"Error loading models: {e}")
     # Create fallback functions if models fail to load
     def mock_function(text):
         return "Model could not be loaded. This is a fallback response."
@@ -77,15 +87,12 @@ class AnalyzeResponse(BaseModel):
     complexity: dict
 
 @app.post("/humanize", response_model=HumanizeResponse)
-async def humanize_text(request: TextRequest):
+async def humanize_text(request: TextRequest, hf_token: str = Depends(get_hf_api_token)):
     input_text = request.text
     
     try:
-        API_URL = "https://api-inference.huggingface.co/models/danibor/flan-t5-base-humanizer"
-        headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
-
-        response = requests.post(API_URL, headers=headers, json={"inputs": text})
-        humanized_text = response.json()[0]["generated_text"]
+        # Generate humanized text using Hugging Face API
+        humanized_text = get_humanized_text(input_text, hf_token)
         
         # Get the differences
         diff = get_diff(input_text, humanized_text)