Update app.py

app.py

@@ -1,55 +1,55 @@
-import easyocr 
-from gradio_client import Client, handle_file
-import pandas as pd
-import gradio as gr
-
-clientImg = Client("dj-dawgs-ipd/IPD-Image-ViT-Finetune")
-clientEngText = Client("dj-dawgs-ipd/IPD-Text-English-Finetune")
-clientHingText = Client("dj-dawgs-ipd/IPD-Text-Hinglish")
-
-profanity_df = pd.read_csv('Hinglish_Profanity_List.csv' , encoding = 'utf-8')
-profanity_hn = profanity_df['profanity_hn']
-
-def extract_text(image):
-    reader = easyocr.Reader(['en'])
-    data = [result[1] for result in reader.readtext(image)]
-    return ' '.join([l for l in data])
-
-def predict(image):
-
-    imgResult = clientImg.predict(
-    		image=handle_file(image),
-    		api_name="/predict"
-    )
-    
-    if float(imgResult[1]['label']) > 0.95:
-        return ["hate" , imgResult[0]]
-    else:
-        
-        ocr_text = extract_text(image)
-        
-        engResult = clientEngText.predict(
-		text=ocr_text,
-		api_name="/classify_text"
-        )
-        
-        hingResult = clientHingText.predict(
-        		text=ocr_text,
-        		api_name="/predict"
-        )
-        
-        if engResult[0] == "NEITHER" or hingResult[0] == "NAG":
-            return ["not_hate" , None]
-        else:
-            return ["hate" , None]
-        
-iface = gr.Interface(fn=predict,
-                     inputs = gr.Image(type='pil'),
-                     outputs=[gr.Label(label = "Class") , gr.Label(label = "Hate Symbol(if any)")],
-                     title = "Hate Speech Detection in Image",
-                     description = "Detect hateful symbols or text in Image"
-        )
-                     
-if __name__ == "__main__":
-    iface.launch()
-
+import easyocr 
+from gradio_client import Client, handle_file
+import pandas as pd
+import gradio as gr
+
+clientImg = Client("dj-dawgs-ipd/IPD-Image-ViT-Finetune")
+clientEngText = Client("dj-dawgs-ipd/IPD-Text-English-Finetune")
+clientHingText = Client("dj-dawgs-ipd/IPD-Text-Hinglish")
+
+profanity_df = pd.read_csv('Hinglish_Profanity_List.csv' , encoding = 'utf-8')
+profanity_hn = profanity_df['profanity_hn']
+
+def extract_text(image):
+    reader = easyocr.Reader(['en'])
+    data = [result[1] for result in reader.readtext(image)]
+    return ' '.join([l for l in data])
+
+def predict(image):
+
+    imgResult = clientImg.predict(
+    		image,
+    		api_name="/predict"
+    )
+    
+    if float(imgResult[1]['label']) > 0.95:
+        return ["hate" , imgResult[0]]
+    else:
+        
+        ocr_text = extract_text(image)
+        
+        engResult = clientEngText.predict(
+		text=ocr_text,
+		api_name="/classify_text"
+        )
+        
+        hingResult = clientHingText.predict(
+        		text=ocr_text,
+        		api_name="/predict"
+        )
+        
+        if engResult[0] == "NEITHER" or hingResult[0] == "NAG":
+            return ["not_hate" , None]
+        else:
+            return ["hate" , None]
+        
+iface = gr.Interface(fn=predict,
+                     inputs = gr.Image(type='pil'),
+                     outputs=[gr.Label(label = "Class") , gr.Label(label = "Hate Symbol(if any)")],
+                     title = "Hate Speech Detection in Image",
+                     description = "Detect hateful symbols or text in Image"
+        )
+                     
+if __name__ == "__main__":
+    iface.launch()
+