Rename gradio_app.py to app.py

gradio_app.py → app.py

@@ -1,9 +1,11 @@
-from transformers import AutoModelForSequenceClassification
-from transformers import TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer, AutoConfig
+# Import the required Libraries
+import gradio as gr
 import numpy as np
+import pandas as pd
+import pickle
+import transformers
+from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification,TFAutoModelForSequenceClassification, pipeline
 from scipy.special import softmax
-import gradio as gr
 
 # Requirements
 model_path = "KwabenaMufasa/Finetuned-Distilbert-base-model"
@@ -11,45 +13,39 @@ tokenizer = AutoTokenizer.from_pretrained(model_path)
 config = AutoConfig.from_pretrained(model_path)
 model = AutoModelForSequenceClassification.from_pretrained(model_path)
 
-
-# #Preprocess text (username and link placeholders)
+#Preprocess text
 def preprocess(text):
     new_text = []
     for t in text.split(" "):
-        t = '@user' if t.startswith('@') and len(t) > 1 else t
-        t = 'http' if t.startswith('http') else t
+        t = "@user" if t.startswith("@") and len(t) > 1 else t
+        t = "http" if t.startswith("http") else t
         new_text.append(t)
     return " ".join(new_text)
 
-
+#Process the input and return prediction
 def sentiment_analysis(text):
     text = preprocess(text)
 
-    # PyTorch-based models
-    encoded_input = tokenizer(text, return_tensors='pt')
+    encoded_input = tokenizer(text, return_tensors = "pt") # for PyTorch-based models
     output = model(**encoded_input)
     scores_ = output[0][0].detach().numpy()
     scores_ = softmax(scores_)
     
     # Format output dict of scores
-    labels = ['Negative', 'Neutral', 'Positive']
+    labels = ["Negative", "Neutral", "Positive"]
     scores = {l:float(s) for (l,s) in zip(labels, scores_) }
     
     return scores
-
-
-demo = gr.Interface(
-    fn=sentiment_analysis, 
-    inputs=gr.Textbox(placeholder="Write your tweet here..."), 
-    outputs="label", 
-    interpretation="default",
-    examples=[["This is Spectacular!"]])
-
     
 
-
-demo.launch(server_name = "0.0.0.0.", server_port = 7860)
-
-if __name__=="__gradio_app__":
-    run()
-
+#Gradio app interface
+app = gr.Interface(fn = sentiment_analysis,
+                   inputs = gr.Textbox("Write your text or tweet here"),
+                   outputs = "label",
+                   title = "Twitter Sentiment Analyzer App",
+                   description  = "Vaccinate or Do Not Vaccinate",
+                   interpretation = "default",
+                   examples = [["Being vaccinated is actually awesome :)"]]
+                   )
+
+app.launch()