Delete app.py

app.py

@@ -1,198 +0,0 @@
-import streamlit as st
-import pandas as pd
-import torch
-from transformers import pipeline, AutoTokenizer
-import matplotlib.pyplot as plt
-from wordcloud import WordCloud
-
-# Load the fine-tuned DistilBERT model from Hugging Face
-MODEL_NAME = "dinusha11/finetuned-distilbert-news"
-
-
-# Load tokenizer and classification pipeline
-@st.cache_resource
-def load_model():
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-    classifier = pipeline("text-classification", model=MODEL_NAME, tokenizer=tokenizer,
-                          device=0 if torch.cuda.is_available() else -1)
-    return classifier
-
-
-classifier = load_model()
-
-
-# Load QA pipeline
-@st.cache_resource
-def load_qa_pipeline():
-    return pipeline("question-answering")
-
-
-qa_pipeline = load_qa_pipeline()
-
-
-# Load Sentiment Analysis pipeline
-@st.cache_resource
-def load_sentiment_pipeline():
-    return pipeline("sentiment-analysis")
-
-
-sentiment_pipeline = load_sentiment_pipeline()
-
-
-# Function to preprocess text
-def preprocess_text(text):
-    return text.strip()
-
-
-# Function for Q&A
-def get_answer(question, context):
-    return qa_pipeline(question=question, context=context)['answer']
-
-
-# Function to generate word cloud
-def generate_wordcloud(text):
-    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
-    return wordcloud
-
-
-# Function to analyze sentiment
-def analyze_sentiment(text):
-    return sentiment_pipeline(text[:512])[0]['label']
-
-
-# Custom CSS Styling
-st.markdown("""
-    <style>
-        body {
-            font-family: Arial, sans-serif;
-            background-color: #f8f9fa;
-        }
-        .css-1aumxhk {
-            display: none;
-        }
-        .main-title {
-            text-align: center;
-            font-size: 36px;
-            color: #2b2d42;
-        }
-        .stButton>button {
-            width: 100%;
-            border-radius: 10px;
-        }
-    </style>
-""", unsafe_allow_html=True)
-
-# Sidebar Navigation
-
-st.sidebar.title("Navigation")
-page = st.sidebar.radio("Go to:", ["Home", "News Classification", "Q&A", "Word Cloud", "Sentiment Analysis"])
-
-# Home Page
-if page == "Home":
-    st.title("📰 News Classification & Analysis App")
-    st.write("Welcome to the AI-powered news classification and analysis platform.")
-
-    st.write("""
-        - 📌 **Upload a CSV** containing news articles.
-        - 🔍 **Get Classification** into Business, Opinion, Political Gossip, Sports, or World News.
-        - 🧠 **Ask AI Questions** on news content.
-        - ☁ **Visualize Data** with a Word Cloud.
-        - 📊 **Analyze Sentiment** of news articles.
-    """)
-    st.success("Get started by navigating to 'News Classification' from the sidebar!")
-
-# News Classification Page
-elif page == "News Classification":
-    st.title("📝 Classify News Articles")
-    uploaded_file = st.file_uploader("📂 Upload a CSV file", type=["csv"], key="file_uploader")
-
-    if uploaded_file:
-        df = pd.read_csv(uploaded_file)
-        if 'content' not in df.columns:
-            st.error("The CSV file must contain a 'content' column.")
-        else:
-            df['processed_content'] = df['content'].apply(preprocess_text)
-            df['class'] = df['processed_content'].apply(lambda x: classifier(x[:512])[0]['label'])
-            st.success("✅ Classification completed!")
-
-            with st.expander("📋 View Classified News"):
-                st.dataframe(df[['content', 'class']])
-
-            # Download button
-            output_csv = df[['content', 'class']].to_csv(index=False).encode('utf-8')
-            st.download_button("⬇ Download Classified Data", data=output_csv, file_name="output.csv", mime="text/csv")
-
-# Q&A Section
-elif page == "Q&A":
-    st.title("🧠 Ask Questions About News Content")
-    uploaded_file_qa = st.file_uploader("📂 Upload CSV for Q&A", type=["csv"], key="qa_file_uploader")
-
-    if uploaded_file_qa:
-        df_qa = pd.read_csv(uploaded_file_qa)
-        if 'content' not in df_qa.columns:
-            st.error("The CSV file must contain a 'content' column.")
-        else:
-            st.write("📰 **Available News Articles:**")
-            selected_article = st.selectbox("Select an article", df_qa['content'])
-
-            question = st.text_input("🔍 Ask a question about this article:")
-
-            if question and selected_article.strip():
-                try:
-                    answer = get_answer(question, selected_article)
-                    st.success(f"**Answer:** {answer}")
-                except Exception as e:
-                    st.error(f"Error processing question: {str(e)}")
-
-# Word Cloud Section
-elif page == "Word Cloud":
-    st.title("☁ Word Cloud Visualization")
-
-    uploaded_file_wc = st.file_uploader("📂 Upload CSV for Word Cloud", type=["csv"], key="wc_file_uploader")
-
-    if uploaded_file_wc:
-        df_wc = pd.read_csv(uploaded_file_wc)
-        if 'content' not in df_wc.columns:
-            st.error("The CSV file must contain a 'content' column.")
-        else:
-            all_text = " ".join(df_wc['content'].dropna().astype(str))  # Ensure no NaN values
-            if all_text:
-                wordcloud = generate_wordcloud(all_text)
-                fig, ax = plt.subplots(figsize=(10, 5))
-                ax.imshow(wordcloud, interpolation="bilinear")
-                ax.axis("off")
-                st.pyplot(fig)
-            else:
-                st.error("The 'content' column is empty or contains invalid data.")
-
-# Sentiment Analysis Section
-elif page == "Sentiment Analysis":
-    st.title("📊 Sentiment Analysis")
-
-    uploaded_file_sentiment = st.file_uploader("📂 Upload CSV for Sentiment Analysis", type=["csv"], key="sentiment_file_uploader")
-
-    if uploaded_file_sentiment:
-        df_sentiment = pd.read_csv(uploaded_file_sentiment)
-        if 'content' not in df_sentiment.columns:
-            st.error("The CSV file must contain a 'content' column.")
-        else:
-            df_sentiment['sentiment'] = df_sentiment['content'].apply(lambda x: analyze_sentiment(x[:512]))
-            st.success("✅ Sentiment Analysis Completed!")
-
-            with st.expander("📋 View Sentiment Results"):
-                st.dataframe(df_sentiment[['content', 'sentiment']])
-
-            # Sentiment distribution visualization
-            sentiment_counts = df_sentiment['sentiment'].value_counts()
-            fig, ax = plt.subplots()
-            sentiment_counts.plot(kind='bar', color=['green', 'red', 'gray'], ax=ax)
-            ax.set_title("Sentiment Distribution")
-            ax.set_xlabel("Sentiment")
-            ax.set_ylabel("Count")
-            st.pyplot(fig)
-
-            # Download button
-            output_csv_sentiment = df_sentiment[['content', 'sentiment']].to_csv(index=False).encode('utf-8')
-            st.download_button("⬇ Download Sentiment Data", data=output_csv_sentiment, file_name="sentiment_output.csv", mime="text/csv")
-
-