fix

app.py

@@ -1,37 +1,7 @@
-from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
 import gradio as gr
 
-# Initialization of the BLIP processor and model
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+def greet(name):
+    return "Hello " + name + "!!"
 
-def generate_captions(image, text=""):
-    # Convert the uploaded image to PIL Image
-    raw_image = Image.fromarray(image).convert('RGB')
-    
-    if text:  # Conditional image captioning
-        inputs = processor(raw_image, text, return_tensors="pt")
-    else:  # Unconditional image captioning
-        inputs = processor(raw_image, return_tensors="pt")
-    
-    # Generate captions for the image
-    out = model.generate(**inputs)
-    caption = processor.decode(out[0], skip_special_tokens=True)
-    
-    return caption
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=generate_captions,
-    inputs=[
-        gr.Image(label="Upload/Drag Image"),  # Removed the 'tool' argument
-        gr.Textbox(label="Conditional Text (optional)", placeholder="Enter conditional text (optional)...")
-    ],
-    outputs=gr.Textbox(label="Generated Caption"),
-    title="BLIP Image Caption Generator",
-    description="This app generates captions for uploaded images. You can also provide conditional text to guide the caption generation."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()

requirements.txt

@@ -1,5 +1,5 @@
-diffusers
-gradio
-torch
-transformers
-Pillow
+# diffusers
+# gradio
+# torch
+# transformers
+# Pillow