Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from transformers import pipeline
+from sklearn.metrics.pairwise import cosine_similarity
+from sentence_transformers import SentenceTransformer
+import string
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+import nltk
+
+# Download NLTK resources (run this once if not already downloaded)
+nltk.download('punkt')
+nltk.download('punkt_tab')
+nltk.download('stopwords')
+nltk.download('wordnet')
+
+# Set modern page configuration
+st.set_page_config(page_title="News Analyzer", layout="wide")
+
+# Inject custom CSS for sleek dark blue theme with black fonts
+st.markdown("""
+    <style>
+    /* Global Styling */
+    body {
+        background: #0b132b;
+        font-family: 'Arial', sans-serif;
+        margin: 0;
+        padding: 0;
+    }
+
+    /* Header Styling */
+    .custom-header {
+        background: linear-gradient(to right, #1f4068, #1b1b2f);
+        padding: 1.5rem;
+        margin-bottom: 1.5rem;
+        border-radius: 12px;
+        text-align: center;
+        font-size: 30px;
+        font-weight: bold;
+        box-shadow: 0px 4px 15px rgba(0, 217, 255, 0.3);
+    }
+
+    /* Buttons */
+    .stButton>button {
+        background: linear-gradient(45deg, #0072ff, #00c6ff);
+        border-radius: 8px;
+        padding: 14px 28px;
+        font-size: 18px;
+        transition: 0.3s ease;
+        border: none;
+    }
+    .stButton>button:hover {
+        transform: scale(1.05);
+        box-shadow: 0px 4px 10px rgba(0, 255, 255, 0.5);
+    }
+
+    /* Text Input */
+    .stTextInput>div>div>input {
+        background-color: rgba(255, 255, 255, 0.1);
+        border-radius: 8px;
+        padding: 12px;
+        font-size: 18px;
+    }
+
+    /* Dataframe Container */
+    .dataframe-container {
+        background: rgba(255, 255, 255, 0.1);
+        padding: 15px;
+        border-radius: 12px;
+    }
+
+    /* Answer Display Box - Larger */
+    .answer-box {
+        background: rgba(0, 217, 255, 0.15);
+        padding: 35px;
+        border-radius: 15px;
+        border: 2px solid rgba(0, 217, 255, 0.6);
+        font-size: 22px;
+        text-align: center;
+        margin-bottom: 20px;
+        min-height: 150px;
+        box-shadow: 0px 2px 12px rgba(0, 217, 255, 0.3);
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        transition: all 0.3s ease;
+    }
+
+    /* CSV Display Box */
+    .csv-box {
+        background: rgba(255, 255, 255, 0.1);
+        padding: 15px;
+        border-radius: 12px;
+        margin-top: 20px;
+        box-shadow: 0px 2px 12px rgba(0, 217, 255, 0.3);
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Modern Header
+st.markdown("<div class='custom-header'> 🧩 AI-Powered News Analyzer</div>", unsafe_allow_html=True)
+
+# Load the Hugging Face models
+classifier = pipeline("text-classification", model="Sandini/news-classifier")  # Classification pipeline
+qa_pipeline = pipeline("question-answering", model="distilbert/distilbert-base-cased-distilled-squad")  # QA pipeline
+
+# Initialize sentence transformer model for QA similarity
+sentence_model = SentenceTransformer('all-MiniLM-L6-v2')  # Pre-trained sentence model
+
+# Define preprocessing functions for classification
+def preprocess_text(text):
+    # Step 1: Lowercase the text
+    text = text.lower()
+
+    # Step 2: Remove punctuation
+    text = text.translate(str.maketrans('', '', string.punctuation))
+
+    # Step 3: Tokenize the text
+    tokens = word_tokenize(text)
+
+    # Step 4: Remove stopwords
+    stop_words = set(stopwords.words('english'))
+    tokens = [word for word in tokens if word not in stop_words]
+
+    # Step 5: Lemmatization
+    lemmatizer = WordNetLemmatizer()
+    tokens = [lemmatizer.lemmatize(word) for word in tokens]
+
+    # Step 6: Join tokens back into a single string
+    preprocessed_text = " ".join(tokens)
+
+    return preprocessed_text
+
+# Reverse mapping (numeric label -> category name)
+label_mapping = {
+    "Business": 0,
+    "Opinion": 1,
+    "Sports": 2,
+    "Political_gossip": 3,
+    "World_news": 4
+}
+reverse_label_mapping = {v: k for k, v in label_mapping.items()}
+
+# Define a function to predict the category for a single text
+def predict_category(text):
+    prediction = classifier(text)
+    predicted_label_id = int(prediction[0]['label'].split('_')[-1])  # Extract numeric label from 'LABEL_X'
+    return reverse_label_mapping[predicted_label_id]
+
+# Responsive Layout - Uses full width
+col1, col2 = st.columns([1.1, 1])
+
+# Left Section - File Upload & CSV/Excel Display
+with col1:
+    st.subheader("📂 Upload News Data")
+    uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"])
+
+    if uploaded_file is not None:
+        # Determine the file extension
+        file_extension = uploaded_file.name.split('.')[-1]
+
+        if file_extension == 'csv':
+            df = pd.read_csv(uploaded_file)
+        elif file_extension == 'xlsx':
+            df = pd.read_excel(uploaded_file)
+
+        # Preprocess the content column and predict categories
+        if 'content' in df.columns:
+            df['preprocessed_content'] = df['content'].apply(preprocess_text)
+            df['class'] = df['preprocessed_content'].apply(predict_category)
+
+            # Drop the preprocessed_content column before displaying or saving
+            df_for_display = df.drop(columns=['preprocessed_content'], errors='ignore')
+            df_for_download = df.drop(columns=['preprocessed_content'], errors='ignore')
+
+        # Download button
+        st.download_button(
+            label="⬇️ Download Processed Data",
+            data=df_for_download.to_csv(index=False).encode('utf-8'),
+            file_name="output.csv",
+            mime="text/csv"
+        )
+
+        # CSV Preview Box
+        st.markdown("<div class='csv-box'><h4>📜 CSV/Excel Preview</h4></div>", unsafe_allow_html=True)
+        st.dataframe(df_for_display, use_container_width=True)
+
+
+# Right Section - Q&A Interface
+with col2:
+    st.subheader("🤖 AI Assistant")
+
+    # Answer Display Box (Initially Empty)
+    answer_placeholder = st.empty()
+    answer_placeholder.markdown("<div class='answer-box'></div>", unsafe_allow_html=True)
+
+    # Question Input
+    st.markdown("### 🔍 Ask Your Question:")
+    user_question = st.text_input("Enter your question here", label_visibility="hidden")  # Hides the label
+
+    # Button & Answer Display
+    if st.button("🔮 Get Answer"):
+        if user_question.strip() and uploaded_file is not None:
+            # Ensure the DataFrame has the required content column
+            if 'content' in df.columns:
+                context = df['content'].dropna().tolist()  # Use the content column as context
+
+                # Generate embeddings for the context and the question
+                context_embeddings = sentence_model.encode(context)
+                question_embedding = sentence_model.encode([user_question])
+
+                # Calculate cosine similarity
+                similarities = cosine_similarity(question_embedding, context_embeddings)
+                top_indices = similarities[0].argsort()[-5:][::-1]  # Get top 5 similar rows
+
+                # Prepare the top 5 similar context rows
+                top_context = "\n".join([context[i] for i in top_indices])
+
+                # Get answer from Hugging Face model using top context
+                result = qa_pipeline(question=user_question, context=top_context)
+                answer = result['answer']
+            else:
+                answer = "⚠️ File does not contain a 'content' column!"
+        else:
+            answer = "⚠️ Please upload a valid file first!"
+
+        answer_placeholder.markdown(f"<div class='answer-box'>{answer}</div>", unsafe_allow_html=True)
+