Minor Modifications

app.py

@@ -1,57 +0,0 @@
-import tensorflow as tf
-import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.text import tokenizer_from_json
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import json
-import pickle
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-
-app = FastAPI(title="News Source Classifier")
-
-try:
-    model = load_model('news_classifier.h5')
-    
-    with open('tokenizer.json') as f:
-        tokenizer_data = json.load(f)
-        tokenizer = tokenizer_from_json(tokenizer_data)
-        
-    with open('vectorizer.pkl', 'rb') as f:
-        vectorizer = pickle.load(f)
-except Exception as e:
-    print(f"Error loading model: {str(e)}")
-    raise
-
-class PredictionRequest(BaseModel):
-    text: str
-
-class PredictionResponse(BaseModel):
-    source: str
-    confidence: float
-
-@app.post("/predict", response_model=PredictionResponse)
-async def predict(request: PredictionRequest):
-    try:
-        sequence = tokenizer.texts_to_sequences([request.text])
-        padded = pad_sequences(sequence, maxlen=100)
-
-        prediction = model.predict(padded)
-        confidence = float(np.max(prediction))
-        
-        predicted_class = int(np.argmax(prediction))
-        source = 'foxnews' if predicted_class == 0 else 'nbc'
-        
-        return PredictionResponse(
-            source=source,
-            confidence=confidence
-        )
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/")
-async def root():
-    return {
-        "message": "News Source Classifier API",
-        "usage": "Make a POST request to /predict with a JSON payload containing 'text' field"
-    }

pipeline.py

@@ -1,4 +1,3 @@
-# pipeline.py
 from transformers import Pipeline
 import tensorflow as tf
 from tensorflow.keras.models import load_model