cryptweet file added

app.py

@@ -1,4 +1,18 @@
 import streamlit as st
+import cryptweet
+st.set_page_config(page_title="Cryptweets",page_icon='https://preview.pixlr.com/images/800wm/100/1/1001457242.jpg')
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.title('Cryptweets')
+st.header("Using Twitter API to predict future movement of cryptocurrencies and stocks")
+test='''It is known that social media platforms like twitter plays an imortant role in
+ determining the price of financial asset platforms like stock market and crytpocurrencies. It is 
+ expected to increase the price of Doge after a tweet from Elon musk. Here the sentiment of public
+  about a cryptocoin or stock (or any current issue going on!) could be calculated with the help of 
+  twitter api and huggingface transformers and a little mathematics'''
+
+st.subheader(test)
+key = st.text_input(label="Enter the keyword eg:#bitcoin ",)
+
+while key!='':
+    st.subheader('the given keyword is '+cryptweet.sentimentanalyser(key))
+    break

cryptweet.py

@@ -0,0 +1,66 @@
+import tweepy
+import json
+!pip -q install transformers
+!pip3 install emoji
+import emoji
+import transformers
+from transformers import pipeline
+
+sentimentmodel=pipeline(model="finiteautomata/bertweet-base-sentiment-analysis")
+
+
+
+# authenticate the api key and token with twitter
+
+from tweepy.auth import OAuthHandler
+
+auth = OAuthHandler('t6d8uj4w9P6DjrNSmfSd42gEI', 'ZLcVXunCbN6NO4rUup6vTR33rO32epm0LFHLkzFLZnhjbQmQzZ')
+auth.set_access_token('1405483945379074054-ktOMmQ6HUcZwzOxiHfjcFmP74hkTn5', 'hV792qfEAuJcvZmkzmoMf61qaaSnoV8D2YiIFYjzAgr06')
+
+
+api = tweepy.API(auth,wait_on_rate_limit=True)
+
+
+# extract the tweets with the given keyword
+
+def tweextractor(keyword):
+  tweet=[]      #list to store tweets extracted
+ 
+  # search_tweets method extract atmost 100 tweets with given keyword
+  # use it with cursor to extract more than that in one go
+  
+  # take 500 popular tweets and 500 recent tweets to keep consistency in the result
+
+  for i in tweepy.Cursor(api.search_tweets, keyword,tweet_mode="extended",lang='en',result_type='recent',count=100).items(500):
+    i = json.dumps(i._json)
+    i = json.loads(i)
+    if i['full_text'] not in tweet:
+      tweet.append(i['full_text'])
+  for i in tweepy.Cursor(api.search_tweets, keyword,tweet_mode="extended",lang='en',result_type='popular',count=100).items(500):
+    i = json.dumps(i._json)
+    i = json.loads(i)
+    if i['full_text'] not in tweet and len(i['full_text'])<500:
+      tweet.append(i['full_text'])
+  print(len(tweet),'tweets total tweets found')
+  return tweet
+  
+
+
+# method to take keyword and find overall sentiment
+
+def sentimentanalyser(keyword):
+  tweets=tweextractor(keyword)
+  score=[]
+  mood=sentimentmodel(tweet)
+  for i in mood:
+    if i['label']=='POS':
+      score.append(i['score'])
+    elif i['label']=='NEG':
+      score.append(-i['score'])
+  sentiment=sum(score)/len(score)
+  if sentiment>0:
+    return 'Sentiment of public is positive with probability '+sentiment
+  else: return 'Sentiment of public is negative with probability'+(-sentiment)
+
+
+