Creating the backup file and changing app.py for DB purpose

app.py

@@ -1,21 +1,21 @@
 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://GMP-21-03:groupa2025@cluster1.u1zed.mongodb.net/?retryWrites=true&w=majority&appName=Cluster1")
+    client = MongoClient("mongodb+srv://groupA:pythongroupA@sentimentcluster.4usfj.mongodb.net/?retryWrites=true&w=majority&appName=SentimentCluster")
     db = client["sentiment_db"]
     return db["tweets"]
 
-#### *3. Load and Process Dataset*
-import pandas as pd
-
-# Load dataset
-df = pd.read_csv("https://huggingface.co/spaces/sharangrav24/SentimentAnalysis/resolve/main/sentiment140.csv")
-
-#### *4. Sentiment Analysis using BERT-ROBERTA*
+collection = get_mongo_client()
 
-from transformers import pipeline
+#### **2. Load Dataset from Hugging Face**
+csv_url = "https://huggingface.co/spaces/sharangrav24/SentimentAnalysis/resolve/main/sentiment140.csv"
+df = pd.read_csv(csv_url)
 
-# Load Hugging Face model
+#### **3. Sentiment Analysis using BERT-ROBERTA**
 sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
 
 # Function to analyze sentiment
@@ -24,16 +24,20 @@ def analyze_sentiment(text):
 
 df["sentiment"] = df["text"].apply(analyze_sentiment)
 
-# Save results to MongoDB
-collection = get_mongo_client()
+#### **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*
-import streamlit as st
-
+#### **5. Build Streamlit Dashboard**
 st.title("Sentiment Analysis Dashboard")
 
-if st.button("Show Data"):
+# 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"):

backup.py

@@ -0,0 +1,41 @@
+from pymongo import MongoClient
+
+def get_mongo_client():
+    client = MongoClient("mongodb+srv://GMP-21-03:[email protected]/?retryWrites=true&w=majority&appName=Cluster1")
+    db = client["sentiment_db"]
+    return db["tweets"]
+
+#### *3. Load and Process Dataset*
+import pandas as pd
+
+# Load dataset
+df = pd.read_csv("https://huggingface.co/spaces/sharangrav24/SentimentAnalysis/resolve/main/sentiment140.csv")
+
+#### *4. Sentiment Analysis using BERT-ROBERTA*
+
+from transformers import pipeline
+
+# Load Hugging Face model
+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)
+
+# Save results to MongoDB
+collection = get_mongo_client()
+collection.insert_many(df.to_dict("records"))
+
+#### *5. Build Streamlit Dashboard*
+import streamlit as st
+
+st.title("Sentiment Analysis Dashboard")
+
+if st.button("Show Data"):
+    st.write(df)
+
+if st.button("Show MongoDB Data"):
+    data = list(collection.find({}, {"_id": 0}))
+    st.write(pd.DataFrame(data))