app.py

pip install torch

import streamlit as st
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the fine-tuned model
MODEL_NAME = "dinusha11/finetuned-distilbert-news"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

# Define label mapping
LABEL_MAPPING = {0: "Business", 1: "Opinion", 2: "Sports", 3: "Political_gossip", 4: "World_news"}

# Function to classify text
def classify_text(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
    predicted_class = torch.argmax(outputs.logits, dim=1).item()
    return LABEL_MAPPING[predicted_class]

# Streamlit UI
st.title("News Classification App")
st.write("Upload a CSV file with news excerpts, and the model will classify each record.")

# File uploader
uploaded_file = st.file_uploader("Upload CSV", type=["csv"])

if uploaded_file:
    df = pd.read_csv(uploaded_file)
    
    if "text" not in df.columns:
        st.error("CSV must contain a 'text' column.")
    else:
        # Preprocess text
        df["text"] = df["text"].fillna("").str.strip().str.lower()
        
        # Apply classification
        df["class"] = df["text"].apply(classify_text)
        
        # Download output CSV
        output_csv = df.to_csv(index=False).encode("utf-8")
        st.download_button("Download Results", data=output_csv, file_name="output.csv", mime="text/csv")

        st.write("Classification Complete! Download your file above.")