Upload 3 files

app.py

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+import joblib
+import numpy as np
+import re
+import string
+from nltk.corpus import stopwords
+from nltk.stem import PorterStemmer
+from nltk.tokenize import TweetTokenizer
+import nltk
+import pickle
+import gradio as gr
+
+with open("freqs.pkl","rb") as fp:
+  freqs = pickle.load(fp)
+fp.close()
+model = joblib.load("C:\\Users\\aryan\\OneDrive\\Desktop\\sentiment\\sentiment1.pkl")
+
+nltk.download("stopwords")
+stop = stopwords.words("english")
+punc = string.punctuation
+def process_tweet(tweet):
+  stemmer = PorterStemmer()
+  tweet2 = re.sub(r'^RT[\s]+',"",tweet)
+  tweet2 = re.sub(r'https?://[^\s\n\r]+','',tweet2)
+  tweet2 = re.sub(r'#','',tweet2)
+  tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True, reduce_len=True)
+  tokens = tokenizer.tokenize(tweet2)
+  tokens_new = []
+  for word in tokens:
+    if (word not in stop and word not in punc):
+      tokens_new.append(stemmer.stem(word))
+    else:
+      continue
+  return tokens_new
+
+def extract_features(tweets, freqs):
+  m = len(tweets)
+  original_row = np.array([1,0,0])
+  x = np.tile(original_row, (m, 1))
+  count = 0
+  for i in range(0,m):
+    for word in process_tweet(tweets[i]):
+      x[i][1]+=freqs.get((word,1),0)
+      x[i][2]+=freqs.get((word,0),0)
+  if "not" in tweets[0]:
+    if(x[0][1]>x[0][2]): x[0][2]=x[0][1]+50
+    else: x[0][1]=x[0][2]+50
+  return x
+
+def predict(tweet,freqs=freqs):
+  arr = [tweet]
+  x= extract_features(arr,freqs)
+  res = model.predict(x)
+  if (res==0): return "Negative comment"
+  else: return "Positive comment"
+
+tweet = gr.Textbox(placeholder="Enter your tweet here")
+int = gr.Interface(fn = predict, inputs = tweet, outputs = "textbox").launch(share = True)
+
+
+

freqs.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df533ac131097d72b6f8fe76300460151b48e6ca531fbfaa4252be7cd9051a7c
+size 314822

sentiment1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1482ddb613a544cd3307989f12554932cd7e87195d8dbc049c5f949106182ea0
+size 847