Upload 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 numpy as np
+##
+# 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;
+        color: black;
+        font-family: 'Arial', sans-serif;
+    }
+    
+    /* Header Styling */
+    .custom-header {
+        background: linear-gradient(to right, #1f4068, #1b1b2f);
+        padding: 1.5rem;
+        border-radius: 12px;
+        text-align: center;
+        color: white;
+        font-size: 30px;
+        font-weight: bold;
+        box-shadow: 0px 4px 15px rgba(0, 217, 255, 0.3);
+    }
+    /* Card Container */
+    .glass-container {
+        background: rgba(255, 255, 255, 0.08);
+        border-radius: 15px;
+        padding: 25px;
+        backdrop-filter: blur(15px);
+        box-shadow: 0px 4px 20px rgba(0, 217, 255, 0.2);
+        transition: transform 0.3s ease-in-out;
+    }
+    .glass-container:hover {
+        transform: scale(1.02);
+    }
+    /* Buttons */
+    .stButton>button {
+        background: linear-gradient(45deg, #0072ff, #00c6ff);
+        color: black;
+        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;
+        color: black;
+        padding: 12px;
+        font-size: 18px;
+    }
+    /* Dataframe Container */
+    .dataframe-container {
+        background: rgba(255, 255, 255, 0.1);
+        padding: 15px;
+        border-radius: 12px;
+        color: black;
+    }
+    /* 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);
+        color: black;
+        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 model
+pipe = pipeline("question-answering", model="distilbert/distilbert-base-cased-distilled-squad")
+
+# Initialize sentence transformer model
+sentence_model = SentenceTransformer('all-MiniLM-L6-v2')  # Pre-trained sentence model
+
+# Responsive Layout - Uses full width
+col1, col2 = st.columns([1.1, 1])
+
+# Left Section - File Upload & CSV/Excel Display
+with col1:
+    st.markdown("<div class='glass-container'>", unsafe_allow_html=True)
+    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)
+        
+        # Download button
+        st.download_button(
+            label="⬇️ Download Processed Data",
+            data=df.to_csv(index=False).encode('utf-8'),
+            file_name="output.csv",
+            mime="text/csv"
+        )
+
+        # CSV Preview Box
+        st.markdown("<div class='csv-box'><h4 style='color: black;'>📜 CSV/Excel Preview</h4>", unsafe_allow_html=True)
+        st.dataframe(df, use_container_width=True)
+        st.markdown("</div>", unsafe_allow_html=True)
+
+    st.markdown("</div>", unsafe_allow_html=True)
+
+# Right Section - Q&A Interface
+with col2:
+    st.markdown("<div class='glass-container'>", unsafe_allow_html=True)
+    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:
+            # Extract the 3rd column from the dataframe
+            context = df.iloc[:, 2].dropna().tolist()  # Get the 3rd column as context (0-based index)
+            
+            # Generate embeddings for the context (3rd column rows) 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 = pipe(question=user_question, context=top_context)
+            answer = result['answer']
+        else:
+            answer = "⚠️ Please upload a valid file first!"
+
+        answer_placeholder.markdown(f"<div class='answer-box'>{answer}</div>", unsafe_allow_html=True)
+
+    st.markdown("</div>", unsafe_allow_html=True)