fixed some parsing issues, optimized payload delivery to reduce prediction time

this commit includes, some bug fixes and text formatting, to the previous version.

app.py

@@ -1,25 +1,30 @@
 import gradio as gr
 from transformers import pipeline
-
 import warnings
 import logging
 warnings.simplefilter('ignore')
 logging.disable(logging.WARNING)
 
-model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
+
 
 
 def predict(image):
     cap = pipeline('image-to-text')
-    genereated_dict = cap(image)
+    caption = cap(image)
+
 
-    final = str(genereated_dict)
-    def sentiment_analysis(phrase):
+    def sentiment_analysis(shortened):
       pipe = pipeline('text-classification')
-      sentiment = pipe(phrase)
-      return str(senti)
-    senti = sentiment_analysis(final)
-    output = final[20:-2]
+      senti = pipe(shortened)
+      return senti
+    caption_string = str(caption)
+    shortened = caption_string.replace("[{'generated_text': '" , "The image is of, ")
+    shortened = shortened.replace("'}]" , "")
+    sentiment = sentiment_analysis(shortened)
+    sentiment_string = ''.join(str(e) for e in sentiment)
+    formated_senti = sentiment_string.replace("{'label': ", ", The Tone of the image(sentiment) after analysisng the caption is that is it is ")
+    formated_senti = formated_senti.replace("'score': ", "in nature with an average percentage of ")
+    output = shortened + formated_senti[0:-2] + '%'
     return output
 
 
@@ -27,13 +32,12 @@ input = gr.inputs.Image(
     label="Upload your Image and wait for 8-12 seconds!", type='pil', optional=False)
 output = gr.outputs.Textbox(label="Captions")
 
-title = "Content ModX UI "
+title = "Content-Mod API UI "
 
 interface = gr.Interface(
     fn=predict,
     inputs=input,
-    theme="grass",
-    outputs= output,
+    outputs=output,
     title=title,
 
 )