diff --git a/app.py b/app.py
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+++ b/app.py
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+import streamlit as st
+import plotly.graph_objs as go
+import yfinance as yf
+import os
+import collections
+from wordcloud import WordCloud
+
+import tweepy
+from datetime import datetime, timedelta
+import pickle
+import numpy as np
+import matplotlib.pyplot as plt
+
+import warnings
+warnings.filterwarnings('ignore')
+
+import re
+import nltk
+nltk.download("stopwords")
+from nltk.corpus import stopwords
+stop_words = set(stopwords.words("english"))
+from nltk.stem.wordnet import WordNetLemmatizer
+from html.parser import HTMLParser
+from nltk.tokenize import word_tokenize
+
+st.set_page_config(page_title="Dashboard", layout="wide")
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
+import matplotlib.pyplot as plt
+from wordcloud import WordCloud
+import pickle
+
+LemmatizerInstance = WordNetLemmatizer()
+HTMLParserInstance = HTMLParser()
+
+f = open("dict_apostrophe.pickle", "rb")
+apostrophe_dict = pickle.load(f)
+f = open("dict_short.pickle", "rb")
+short_word_dict = pickle.load(f)
+f = open("dict_emoji.pickle", "rb")
+emoticon_dict = pickle.load(f)
+
+with open("model.pickle", "rb") as f:
+    svc_clf = pickle.load(f)
+f.close()
+
+with open("tfidf_vectorizer.pickle", "rb") as f:
+    tfidf_vectorizer = pickle.load(f)
+f.close()
+
+def get_actual_prices(crypto_type, color):
+    ticker = yf.Ticker(f"{crypto_type}-USD")
+    data = ticker.history(period="6d", interval="1m")
+    fig = go.Figure(data=go.Scatter(x=data["Open"].index, 
+                            y=data["Open"].values,
+                            marker_color=color, text="Price(USD)"))
+    fig.update_layout({"title": f'Actual {crypto_type} Prices from {str(min(data.index)).split(" ")[0]} to {str(max(data.index)).split(" ")[0]}',
+                    "xaxis": {"title":"Date"},
+                    "yaxis": {"title":"Price(USD)"},
+                    "showlegend": False})
+    return fig
+
+def FunctionDict(t, d):
+    '''This function splits the text into words and replace the word with the value mapped in the dictionary if present'''
+    for w in t.split():
+        if w.lower() in d:
+            if w.lower() in t.split():
+                t = t.replace(w, d[w.lower()])
+    return t
+
+def get_clean_text(text):
+    cleaned_text = text.replace("\n", " ")
+    cleaned_text = HTMLParserInstance.unescape(cleaned_text)
+    cleaned_text = cleaned_text.lower()
+    cleaned_text = FunctionDict(cleaned_text, apostrophe_dict)
+    cleaned_text = FunctionDict(cleaned_text, short_word_dict)
+    cleaned_text = FunctionDict(cleaned_text, emoticon_dict)
+    cleaned_text = re.sub(r'[^\w\s]',' ', cleaned_text)
+    cleaned_text = re.sub(r'[^a-zA-Z0-9]',' ', cleaned_text)
+    cleaned_text = re.sub(r'[^a-zA-Z]',' ', cleaned_text)
+    cleaned_text = ' '.join([w for w in cleaned_text.split() if len(w)>1])
+    cleaned_text = word_tokenize(cleaned_text)
+    cleaned_text = [w for w in cleaned_text if not w in stop_words]
+    cleaned_text = ' '.join([LemmatizerInstance.lemmatize(i) for i in cleaned_text])
+    return cleaned_text
+
+def scrap_load_data():
+    bearer_token = 'AAAAAAAAAAAAAAAAAAAAAIF%2FfQEAAAAAlRsrX61Bg3Bho%2Fv0n0JW4Ufa8rA%3Dr5WfagCULkXtF8KnVRksOsmp2wM2w6StO1e4XLqNiJ9QlEV7RK'
+    client = tweepy.Client(bearer_token=bearer_token)
+    all_tweets_dict = {}
+    all_counts_dict = {}
+    for i in range(0, 6):
+        start_time = (datetime.now() - timedelta(days=1, hours=6) - timedelta(days=i)).strftime("%Y-%m-%dT%H:%M:%S%ZZ")
+        end_time = (datetime.now() - timedelta(hours=6) - timedelta(days=i)).strftime("%Y-%m-%dT%H:%M:%S%ZZ")
+        queries = ['#Ethereum -is:retweet lang:en', '#Litecoin -is:retweet lang:en', '#Bitcoin -is:retweet lang:en']
+        day_tweets = []
+        day_counts = []
+        all_tweets_dict[end_time.split("T")[0]] = day_tweets
+        all_counts_dict[end_time.split("T")[0]] = day_counts
+        for query in queries:
+            for tweet in tweepy.Paginator(client.search_recent_tweets, query=query, start_time=start_time, end_time=end_time, max_results=100).flatten(limit=50):
+                day_tweets.append(get_clean_text(tweet.text))
+            for counts in client.get_recent_tweets_count(query=query, start_time=start_time, end_time=end_time):
+                if (type(counts) == dict ) & (len(counts) != 0):
+                    day_counts.append(counts.get("total_tweet_count"))
+    all_counts_dict = dict(zip(all_counts_dict.keys(), [sum(i) for i in all_counts_dict.values()]))
+    return all_tweets_dict, all_counts_dict
+
+
+def load_tweets_info():
+    with open("all_tweets_dict.pkl", "rb") as f:
+        all_tweets_dict = pickle.load(f)
+    f.close()
+    with open("all_counts_dict.pkl", "rb") as f:
+        all_counts_dict = pickle.load(f)
+    f.close()
+    return all_tweets_dict, all_counts_dict
+
+
+
+def get_pred_dict(all_tweets_dict):
+    prediction_dict = {}
+    positive_ratio_dict = {}
+    for day in all_tweets_dict.keys():
+        tweets = all_tweets_dict[day]
+        tfidf_tweets = tfidf_vectorizer.transform(tweets)
+        predictions = svc_clf.predict(tfidf_tweets)
+        prediction_dict[day] = predictions
+        positive_ratio_dict[day] = np.count_nonzero(predictions) / len(predictions)
+    return prediction_dict, positive_ratio_dict
+
+
+
+def plot_pos_sent(x, y):
+    fig = go.Figure(data=go.Scatter(x=x, 
+                            y=y,
+                            marker_color='indianred', text="Ratio"))
+    fig.update_layout({"title": f'Positive Sentiment Ratio from {min(x)} to {max(x)}',
+                    "xaxis": {"title":"Date"},
+                    "yaxis": {"title":"Positive Sentiment Ratio"},
+                    "showlegend": False})
+    return fig
+
+
+
+def plot_tweet_count(x, y):
+    fig = go.Figure(data=go.Scatter(x=x, 
+                            y=y,
+                            marker_color='violet', text="Counts"))
+    fig.update_layout({"title": f'Crypto Tweet Counts from {min(x)} to {max(x)}',
+                    "xaxis": {"title":"Date"},
+                    "yaxis": {"title":"Total Tweet Counts"},
+                    "showlegend": False})
+    return fig
+
+def get_donut(data):
+    colors = ['limegreen', '#800080']
+    labels = ["Negative", "Positive"]
+    explode = (0.10, 0)
+    fig, ax = plt.subplots()
+    fig.set_facecolor("#fff9c9")
+    plt.pie(data, labels=labels, colors=colors, explode=explode, autopct="%1.1f%%")
+    centre_circle = plt.Circle((0, 0), 0.60, fc='#fff9c9')
+    fig = plt.gcf()
+    fig.gca().add_artist(centre_circle)
+    return fig
+
+
+def get_wordcloud(text_list):
+    WordString = ' '.join(text_list)
+    wordcloud = WordCloud(background_color="white").generate(WordString)
+    fig = plt.figure()
+    plt.imshow(wordcloud, interpolation="bilinear")
+    plt.axis("off")
+    return fig
+
+
+def display_donuts():
+    if os.path.exists("all_tweets_dict.pkl"):
+
+        all_tweets_dict, _ = load_tweets_info()
+        predictions, _ = get_pred_dict(all_tweets_dict)
+        pie_data = {}
+        for day in predictions.keys():
+            pie_data[day] = (list(predictions[day]).count(0), list(predictions[day]).count(1))
+        pie_data = collections.OrderedDict(sorted(pie_data.items()))
+
+        col1, col2, col3 = st.columns(3)
+        col4, col5, col6 = st.columns(3)
+
+        with col1:
+            st.header(list(pie_data.keys())[0])
+            fig = get_donut(pie_data[list(pie_data.keys())[0]])
+            st.pyplot(fig)
+
+        with col2:
+            st.header(list(pie_data.keys())[1])
+            fig = get_donut(pie_data[list(pie_data.keys())[1]])
+            st.pyplot(fig)
+
+        with col3:
+            st.header(list(pie_data.keys())[2])
+            fig = get_donut(pie_data[list(pie_data.keys())[2]])
+            st.pyplot(fig)
+
+        with col4:
+            st.header(list(pie_data.keys())[3])
+            fig = get_donut(pie_data[list(pie_data.keys())[3]])
+            st.pyplot(fig)
+
+        with col5:
+            st.header(list(pie_data.keys())[4])
+            fig = get_donut(pie_data[list(pie_data.keys())[4]])
+            st.pyplot(fig)
+        with col6:
+            st.header(list(pie_data.keys())[5])
+            fig = get_donut(pie_data[list(pie_data.keys())[5]])
+            st.pyplot(fig)
+    else:
+        st.error("Please scrap the data first!")
+
+def display_wordclouds():
+    if os.path.exists("all_tweets_dict.pkl"):
+
+        all_tweets_dict, _ = load_tweets_info()
+
+        for day in all_tweets_dict.keys():
+            text_list_clean = []
+            for text in all_tweets_dict[day]:
+                text = text.replace(" co ", " ")
+                text_list_clean.append(text)
+            all_tweets_dict[day] = text_list_clean
+
+
+        col1, col2, col3 = st.columns(3)
+        col4, col5, col6 = st.columns(3)
+
+        with col1:
+            st.header(str(sorted(list(all_tweets_dict.keys()))[0]))
+            fig = get_wordcloud(all_tweets_dict[str(sorted(list(all_tweets_dict.keys()))[0])])
+            st.pyplot(fig)
+
+        with col2:
+            st.header(str(sorted(list(all_tweets_dict.keys()))[1]))
+            fig = get_wordcloud(all_tweets_dict[str(sorted(list(all_tweets_dict.keys()))[1])])
+            st.pyplot(fig)
+
+        with col3:
+            st.header(str(sorted(list(all_tweets_dict.keys()))[2]))
+            fig = get_wordcloud(all_tweets_dict[str(sorted(list(all_tweets_dict.keys()))[2])])
+            st.pyplot(fig)
+
+        with col4:
+            st.header(str(sorted(list(all_tweets_dict.keys()))[3]))
+            fig = get_wordcloud(all_tweets_dict[str(sorted(list(all_tweets_dict.keys()))[3])])
+            st.pyplot(fig)
+
+        with col5:
+            st.header(str(sorted(list(all_tweets_dict.keys()))[4]))
+            fig = get_wordcloud(all_tweets_dict[str(sorted(list(all_tweets_dict.keys()))[4])])
+            st.pyplot(fig)
+
+        with col6:
+            st.header(str(sorted(list(all_tweets_dict.keys()))[5]))
+            fig = get_wordcloud(all_tweets_dict[str(sorted(list(all_tweets_dict.keys()))[5])])
+            st.pyplot(fig)
+    else:
+        st.error("Please scrap the data first!")
+
+
+with st.sidebar:
+    title = "Dashboard"
+    st.title(title)
+    st.write("Welcome to the Crypto Sentiment Analysis Dashboard!")
+
+
+
+actual_prices_bar = st.sidebar.radio("Get Actual Crypto Prices:", ("Bitcoin", "Ethereum", "Litecoin"))
+if actual_prices_bar == "Bitcoin":
+    st.plotly_chart(get_actual_prices("BTC", "indianred"), use_container_width=True)
+elif actual_prices_bar == "Ethereum":
+    st.plotly_chart(get_actual_prices("ETH", "green"), use_container_width=True)
+elif actual_prices_bar == "Litecoin":
+    st.plotly_chart(get_actual_prices("LTC", "orange"), use_container_width=True)
+    
+
+scrap_data_bar = st.sidebar.button("Scrap Latest Twitter Data")
+if scrap_data_bar == True:
+    with st.spinner("Scraping data...... (ETA: 10 Seconds)"):
+        all_tweets_dict, all_counts_dict = scrap_load_data()
+        with open("all_tweets_dict.pkl", "wb") as f:
+            pickle.dump(all_tweets_dict, f)
+        f.close()
+        with open("all_counts_dict.pkl", "wb") as f:
+            pickle.dump(all_counts_dict, f)
+        f.close()
+        # time.sleep(2)
+        st.sidebar.success("Successfully scraped. You may use all functions now!")
+
+plot_scrap_bar = st.sidebar.radio("Plot:", ("Positive Sentiment Ratio", "Crypto Tweet Count"))
+if plot_scrap_bar == "Positive Sentiment Ratio":
+    if os.path.exists("all_tweets_dict.pkl"):
+        all_tweets_dict, _ = load_tweets_info()
+        _, pos_ratio_dict = get_pred_dict(all_tweets_dict)
+        x, y = zip(*sorted(pos_ratio_dict.items()))
+        fig = plot_pos_sent(x, y)
+        st.plotly_chart(fig, use_container_width=True)
+    else:
+        st.sidebar.info("Please scrap the data first!")
+if plot_scrap_bar == "Crypto Tweet Count":
+    if os.path.exists("all_tweets_dict.pkl"):
+        _, all_counts_dict = load_tweets_info()
+        x, y = zip(*sorted(all_counts_dict.items()))
+        fig = plot_tweet_count(x,y)
+        st.plotly_chart(fig, use_container_width=True)
+    else:
+        st.sidebar.info("Please scrap the data first!")
+
+
+
+wc_bar = st.sidebar.button("Display Wordclouds", on_click=display_wordclouds)
+if wc_bar == True:
+    if os.path.exists("all_tweets_dict.pkl"):
+        st.sidebar.success("Fetched WordClouds!")
+    else:
+        st.sidebar.error("Need scraped data!!")
+
+donut_bar = st.sidebar.button("Display Pie Donuts", on_click=display_donuts)
+if donut_bar == True:
+    if os.path.exists("all_tweets_dict.pkl"):
+        st.sidebar.success("Fetched Pie Donuts!")
+    else:
+        st.sidebar.error("Need scraped data!!")
+
+
+txt_bar = st.sidebar.text_area('Enter Text to predict:', placeholder="Bitcoin is the best crypto...")
+if txt_bar != "":
+    tfidf_cvt = tfidf_vectorizer.transform([txt_bar])
+    pred = svc_clf.predict(tfidf_cvt)
+    if pred[0] == 1:
+        sentiment = "Positive"
+    else:
+        sentiment = "Negative"
+    st.sidebar.info(f"Sentiment: {sentiment}")
+
+
+
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diff --git a/dict_emoji.pickle b/dict_emoji.pickle
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diff --git a/dict_short.pickle b/dict_short.pickle
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diff --git a/model.pickle b/model.pickle
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diff --git a/requirements.txt b/requirements.txt
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+++ b/requirements.txt
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+
+matplotlib==3.3.4
+nltk==3.6.5
+numpy==1.19.2
+pandas==1.1.5
+plotly==5.10.0
+streamlit==1.10.0
+tweepy==4.6.0
+wordcloud==1.8.2.2
+yfinance==0.1.74
diff --git a/tfidf_vectorizer.pickle b/tfidf_vectorizer.pickle
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