app.py

from pymongo import MongoClient
import pandas as pd
from transformers import pipeline
import streamlit as st

#### **1. MongoDB Connection**
def get_mongo_client():
    client = MongoClient("mongodb+srv://groupA:[email protected]/?retryWrites=true&w=majority&appName=SentimentCluster")
    db = client["sentiment_db"]
    return db["tweets"]

collection = get_mongo_client()

#### **2. Load Dataset from Hugging Face**
csv_url = "https://huggingface.co/spaces/sharangrav24/SentimentAnalysis/resolve/main/sentiment140.csv"
df = pd.read_csv(csv_url)

#### **3. Sentiment Analysis using BERT-ROBERTA**
sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")

# Function to analyze sentiment
def analyze_sentiment(text):
    return sentiment_pipeline(text)[0]['label']

df["sentiment"] = df["text"].apply(analyze_sentiment)

#### **4. Upload Data to MongoDB**
# Convert DataFrame to dictionary and upload to MongoDB
collection.delete_many({})  # Optional: Clear existing data before inserting
collection.insert_many(df.to_dict("records"))

#### **5. Build Streamlit Dashboard**
st.title("Sentiment Analysis Dashboard")

# Show first 5 rows from MongoDB
st.subheader("First 5 Rows from Database")
data = list(collection.find({}, {"_id": 0}).limit(5))
st.write(pd.DataFrame(data))

if st.button("Show Complete Data"):
    st.write(df)

if st.button("Show MongoDB Data"):
    data = list(collection.find({}, {"_id": 0}))
    st.write(pd.DataFrame(data))