Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import re
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+from transformers import pipeline
+from PIL import Image
+
+# Load Models
+news_classifier = pipeline("text-classification", model="Oneli/News_Classification")
+import streamlit as st
+import pandas as pd
+import re
+import string
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.stem import WordNetLemmatizer
+from transformers import pipeline
+from PIL import Image
+
+# Download required NLTK data
+nltk.download('stopwords')
+nltk.download('punkt')
+nltk.download('wordnet')
+
+# Load Models
+news_classifier = pipeline("text-classification", model="Oneli/News_Classification")
+qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
+
+# Label Mapping
+label_mapping = {
+    "LABEL_0": "Business",
+    "LABEL_1": "Opinion",
+    "LABEL_2": "Political Gossip",
+    "LABEL_3": "Sports",
+    "LABEL_4": "World News"
+}
+
+# Store classified article for QA
+context_storage = {"context": "", "bulk_context": "", "num_articles": 0}
+
+
+# Text Cleaning Functions
+def clean_text(text):
+    text = text.lower()
+    text = re.sub(f"[{string.punctuation}]", "", text)  # Remove punctuation
+    text = re.sub(r"[^a-zA-Z0-9\s]", "", text)  # Remove special characters
+    tokens = word_tokenize(text)
+    tokens = [word for word in tokens if word not in stopwords.words("english")]  # Remove stopwords
+    lemmatizer = WordNetLemmatizer()
+    tokens = [lemmatizer.lemmatize(word) for word in tokens]  # Lemmatize tokens
+    return " ".join(tokens)
+
+
+# Define the functions
+def classify_text(text):
+    cleaned_text = clean_text(text)
+    result = news_classifier(cleaned_text)[0]
+    category = label_mapping.get(result['label'], "Unknown")
+    confidence = round(result['score'] * 100, 2)
+
+    # Store context for QA
+    context_storage["context"] = cleaned_text
+
+    return category, f"Confidence: {confidence}%"
+
+
+def classify_csv(file):
+    try:
+        df = pd.read_csv(file, encoding="utf-8")
+        text_column = df.columns[0]  # Assume first column is the text column
+
+        df[text_column] = df[text_column].astype(str).apply(clean_text)  # Clean text column
+        df["Encoded Prediction"] = df[text_column].apply(lambda x: news_classifier(x)[0]['label'])
+        df["Decoded Prediction"] = df["Encoded Prediction"].map(label_mapping)
+        df["Confidence"] = df[text_column].apply(lambda x: round(news_classifier(x)[0]['score'] * 100, 2))
+
+        # Store all text as a single context for QA
+        context_storage["bulk_context"] = " ".join(df[text_column].dropna().astype(str).tolist())
+        context_storage["num_articles"] = len(df)
+
+        output_file = "output.csv"
+        df.to_csv(output_file, index=False)
+        return df, output_file
+    except Exception as e:
+        return None, f"Error: {str(e)}"
+
+
+def chatbot_response(history, user_input, source):
+    user_input = user_input.lower()
+    context = context_storage["context"] if source == "Single Article" else context_storage["bulk_context"]
+    num_articles = context_storage["num_articles"]
+
+    if "number of articles" in user_input or "how many articles" in user_input:
+        answer = f"There are {num_articles} articles in the uploaded CSV."
+        history.append([user_input, answer])
+        return history, ""
+
+    if context:
+        result = qa_pipeline(question=user_input, context=context)
+        answer = result["answer"]
+        history.append([user_input, answer])
+        return history, ""
+
+    responses = {
+        "hello": "👋 Hello! How can I assist you with news today?",
+        "hi": "😊 Hi there! What do you want to know about news?",
+        "how are you": "🤖 I'm just a bot, but I'm here to help!",
+        "thank you": "🙏 You're welcome! Let me know if you need anything else.",
+        "news": "📰 I can classify news into Business, Sports, Politics, and more!",
+    }
+    response = responses.get(user_input,
+                             "🤔 I'm here to help with news classification and general info. Ask me about news topics!")
+    history.append([user_input, response])
+    return history, ""
+
+
+# Streamlit App Layout
+st.set_page_config(page_title="News Classifier", page_icon="📰")
+cover_image = Image.open("cover.png")  # Ensure this image exists
+st.image(cover_image, caption="News Classifier 📢", use_column_width=True)
+
+# Section for Single Article Classification
+st.subheader("📰 Single Article Classification")
+text_input = st.text_area("Enter News Text", placeholder="Type or paste news content here...")
+if st.button("🔍 Classify"):
+    if text_input:
+        category, confidence = classify_text(text_input)
+        st.write(f"*Predicted Category:* {category}")
+        st.write(f"*Confidence Level:* {confidence}")
+    else:
+        st.warning("Please enter some text to classify.")
+
+# Section for Bulk CSV Classification
+st.subheader("📂 Bulk Classification (CSV)")
+file_input = st.file_uploader("Upload CSV File", type="csv")
+if file_input:
+    df, output_file = classify_csv(file_input)
+    if df is not None:
+        st.dataframe(df)
+        st.download_button(
+            label="Download Processed CSV",
+            data=open(output_file, 'rb').read(),
+            file_name=output_file,
+            mime="text/csv"
+        )
+    else:
+        st.error(f"Error processing file: {output_file}")
+
+# Section for Chatbot Interaction
+st.subheader("💬 AI Chat Assistant")
+history = []
+user_input = st.text_input("Ask about news classification or topics", placeholder="Type a message...")
+source_toggle = st.radio("Select Context Source", ["Single Article", "Bulk Classification"])
+if st.button("✉ Send"):
+    history, bot_response = chatbot_response(history, user_input, source_toggle)
+    st.write("*Chatbot Response:*")
+    for q, a in history:
+        st.write(f"*Q:* {q}")
+        st.write(f"*A:* {a}")