Upload 2 files

- Bar chart colour change
- Comparison report

app.py

@@ -11,7 +11,7 @@ import cryptography
 import cryptography.fernet
 
 class Utility(object):
-  
+
   def __init__(self, name="Utility") -> None:
     self.name = name
     self.author = "Duc Haba, Girish"
@@ -20,7 +20,7 @@ class Utility(object):
 
     #Define encrypted keys
     self._huggingface_key="gAAAAABkgtmOIjpnjwXFWmgh1j2et2kMjHUze-ym6h3BieAp34Sqkqv3EVYvRinETvpw-kXu7RSRl5_9FqrYe-7unfakMvMkU8nHrfB3hBSC76ZTXwkVSzlN0RfBNs9NL8BGjaSJ8mz8"
-    
+
     #Key for crypto
     self._fkey="=cvsOPRcWD6JONmdr4Sh6-PqF6nT1InYh965mI8f_sef"
     return
@@ -44,14 +44,14 @@ class Utility(object):
       print(x)
     else:
       y = x
-    return y  
+    return y
 
   # Hugging face : Login to Hugging face
   def _login_hface(self):
-    huggingface_hub.login(self._decrypt_it(self._huggingface_key), 
+    huggingface_hub.login(self._decrypt_it(self._huggingface_key),
       add_to_git_credential=True) # non-blocking login
     self._ph()
-    return 
+    return
 
   # Hugging face : Push files to Hugging face
   def push_hface_files(self,
@@ -63,8 +63,8 @@ class Utility(object):
       for f in hf_names:
         lo = local_dir + f
         huggingface_hub.upload_file(
-          path_or_fileobj=lo, 
-          path_in_repo=f, 
+          path_or_fileobj=lo,
+          path_in_repo=f,
           repo_id=hf_space,
           repo_type=huggingface_hub.REPO_TYPE_SPACE)
     except Exception as e:
@@ -82,10 +82,10 @@ class Utility(object):
 
   # Hugging face : Login to Hugging face
   def _login_hface(self):
-    huggingface_hub.login(self._decrypt_it(self._huggingface_key), 
+    huggingface_hub.login(self._decrypt_it(self._huggingface_key),
       add_to_git_credential=True) # non-blocking login
     self._ph()
-    return 
+    return
 
   # System Info : Fetch available CPU and RAM of the system
   def fetch_system_info(self):
@@ -104,9 +104,9 @@ class Utility(object):
     s += f"Available memory: {mem_available_gb:.2f} GB\n"
     # print(f"Used memory: {mem_used_gb:.2f} GB")
     s += f"Memory usage: {mem_used_gb/mem_total_gb:.2f}%\n"
-    return 
+    return
 
-  # System Info : Fetch GPU information of the system   
+  # System Info : Fetch GPU information of the system
   def fetch_gpu_info(self):
     s=''
     try:
@@ -126,8 +126,8 @@ class Utility(object):
     s += f"Hostname: {hostname}\n"
     s += f"IP Address: {ip_address}\n"
     return s
-    
-  # Create and writes data to the file  
+
+  # Create and writes data to the file
   def write_file(self,fname, txt):
     f = open(fname, "w")
     f.writelines("\n".join(txt))
@@ -140,14 +140,14 @@ class Utility(object):
     if (is_generate):
       s=open(self._xkeyfile, "rb").read()
     return s
-  
+
   # Crypto : Decrypt value
   def _decrypt_it(self, x):
     y = self._fetch_crypt()
     f = cryptography.fernet.Fernet(y)
     m = f.decrypt(x)
     return m.decode()
-  
+
   # Crypto : Encrypt value
   def _encrypt_it(self, x):
     key = self._fetch_crypt()
@@ -156,27 +156,27 @@ class Utility(object):
     y = f.encrypt(p)
     return y
 
-# Add method to class 
+# Add method to class
 def add_method(cls):
   def decorator(func):
-    @functools.wraps(func) 
-    def wrapper(*args, **kwargs): 
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
       return func(*args, **kwargs)
     setattr(cls, func.__name__, wrapper)
     return func # returning func means func can still be used normally
-  return decorator     
-  
+  return decorator
 
 """ This file contains multiple Python classes and responssible to provide Emotions based on the given user input
     Currently it supports emotions like Anger, Joy, Optimism and Sadness"""
 
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from  matplotlib.colors import LinearSegmentedColormap
 import scipy
 import scipy.special
 import pandas
 
 class SentimentAnalyser(object):
-  
+
   global utility
 
   # initialize the object
@@ -206,7 +206,7 @@ class SentimentAnalyser(object):
     modelLink = "bhadresh-savani/distilbert-base-uncased-emotion"
     self.tokenizer = AutoTokenizer.from_pretrained(modelLink)
     self.model = AutoModelForSequenceClassification.from_pretrained(modelLink)
-    return  
+    return
 
 sentiment = SentimentAnalyser(name="EmotionAnalyser")
 
@@ -226,7 +226,8 @@ def _predict_sentiment(p):
 
 @add_method(SentimentAnalyser)
 def draw_bar_plot(df_data, title='Sentiment Analysis', xlabel='p string', ylabel='Emotion Score'):
-  pic = df_data.plot.bar(color=['#84c98c', '#747c0c', '#3fbfbf','#e89096', '#dc545c' , '#a31a0e'],
+  graphCmap=LinearSegmentedColormap.from_list('gr',["g", "w", "r"])
+  pic = df_data.plot.bar(cmap=graphCmap,
         title=title,
         ylabel=ylabel,
         xlabel=xlabel,
@@ -236,28 +237,29 @@ def draw_bar_plot(df_data, title='Sentiment Analysis', xlabel='p string', ylabel
 @add_method(SentimentAnalyser)
 def predict_sentiment(p):
   df_out = _predict_sentiment(p)
-  print("sort : ", df_out.sort_values(['Love', 'Joy', 'Surprise', 'Fear', 'Sadness', 'Anger']))
   max_column = df_out.loc[0].idxmax()
   max_value = df_out.loc[0].max()
   title = f'Sentiment Analysis: {max_column}: {round(max_value*100,1)}%'
   xlabel= f'Input: {p}'
   pic = draw_bar_plot(df_out, title=title, xlabel=xlabel)
-  return pic.get_figure(), df_out.to_json()  
+  return pic.get_figure(), df_out.to_json()
 
 import gradio
 in_box = [gradio.Textbox(lines=1, label="Input", placeholder="type text here")]
 out_box = [gradio.Plot(label="Sentiment Score:"),
   gradio.Textbox(lines=4, label="Raw JSON Response:")]
 title = "Sentiment Analysis: Understanding the Emotional Tone of Text"
-desc = "Sentiment analysis is a powerful tool that can be used to gain insights into how people feel about the world around them." 
+desc = "Sentiment analysis is a powerful tool that can be used to gain insights into how people feel about the world around them."
 exp = [
   ['I am feeling very bad today.'],
   ['I hate to swim early morning.']
- ] 
+ ]
+arti= "We did a comparision of OpenAI vs DestilBert model (which we are currently using in this space) by running 31 sentences in a loop and found DestilBert is 27 times faster than OpenAI."
 
 gradio.Interface(fn=predict_sentiment,
   inputs=in_box,
   outputs=out_box,
   title=title,
   description=desc,
-  examples=exp).launch(debug=True)
+  examples=exp,
+  article=arti).launch(debug=True)