Update app.py

app.py

@@ -1,33 +1,33 @@
-from PIL import Image
-import gradio as gr
-from transformers import BlipProcessor, BlipForConditionalGeneration 
-
-class ImageCaption:
-    def __init__(self):
-        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")  
-        self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
-
-    def generate(self,img):
-        if isinstance(img,str):
-            img =  Image.open(img)
-
-        input = self.processor(img,return_tensors='pt')
-        print(**input)
-        output = self.model.generate(**input)
-        caption = self.processor.decode(output[0],skip_special_tokens = True)
-        return caption
-    
-
-ic = ImageCaption()
-app = gr.Interface(
-    fn = ic.generate,
-    inputs=gr.Image(type='pil'),
-    outputs="text",
-    description="upload image to generate caption"
-
-)
-
-app.launch()
-
-
+from PIL import Image
+import gradio as gr
+from transformers import BlipProcessor, BlipForConditionalGeneration 
+
+class ImageCaption:
+    def __init__(self):
+        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")  
+        self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+    def generate(self,img):
+        if isinstance(img,str):
+            img =  Image.open(img)
+        text = "Explain this image,that what she is doing and what kind of expression is it"
+        input = self.processor(img,text=text,return_tensors='pt')
+        # print(**input)
+        output = self.model.generate(**input)
+        caption = self.processor.decode(output[0],skip_special_tokens = True)
+        return caption
+    
+
+ic = ImageCaption()
+app = gr.Interface(
+    fn = ic.generate,
+    inputs=gr.Image(type='pil'),
+    outputs="text",
+    description="upload image to generate caption"
+
+)
+
+app.launch()
+
+
 # print(ic.generate(input("Enter the source of image: ")))