fixed a bug, and misc changes

app.py

@@ -1,32 +1,47 @@
-import gradio as gr
-from PIL import Image
-import torch
-from transformers import CLIPProcessor, CLIPModel
-
-# Load model and processor
-model = CLIPModel.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
-processor = CLIPProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
-
-def calculate_similarity(image, text_prompt):
-    # Process inputs
-    inputs = processor(text=text_prompt, images=image, return_tensors="pt", padding=True)
-
-    # Forward pass
-    outputs = model(**inputs)
-
-    # Normalize and calculate cosine similarity
-    image_features = outputs.image_embeds / outputs.image_embeds.norm(dim=-1, keepdim=True)
-    text_features = outputs.text_embeds / outputs.text_embeds.norm(dim=-1, keepdim=True)
-    cosine_similarity = torch.nn.functional.cosine_similarity(image_features, text_features)
-
-    return {"Cosine Similarity": cosine_similarity.item()}
-
-# Set up Gradio interface
-iface = gr.Interface(fn=calculate_similarity,
-                     inputs=[gr.inputs.Image(type="pil"), gr.inputs.Textbox(label="Text Prompt")],
-                     outputs=[gr.outputs.Label(label="Cosine Similarity")],
-                     title="OpenClip Cosine Similarity Calculator",
-                     description="Upload an image and provide a text prompt to calculate the cosine similarity.")
-
-# Launch the interface locally for testing
-iface.launch()
+import gradio as gr
+from PIL import Image
+import torch
+from transformers import CLIPProcessor, CLIPModel
+
+# Load model and processor
+model = CLIPModel.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
+processor = CLIPProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
+
+def calculate_similarity(image, text_prompt):
+    # Ensure text_prompt is a string
+    if not isinstance(text_prompt, str):
+        text_prompt = str(text_prompt)
+
+    # Process inputs
+    inputs = processor(images=image, text=text_prompt, return_tensors="pt", padding=True)
+
+    # Forward pass
+    outputs = model(**inputs)
+
+    # Normalize and calculate cosine similarity
+    image_features = outputs.image_embeds / outputs.image_embeds.norm(dim=-1, keepdim=True)
+    text_features = outputs.text_embeds / outputs.text_embeds.norm(dim=-1, keepdim=True)
+    cosine_similarity = torch.nn.functional.cosine_similarity(image_features, text_features)
+    
+    # Adjusting the similarity score
+    adjusted_similarity = cosine_similarity.item() * 3 * 100
+    clipped_similarity = min(adjusted_similarity, 99.99)
+    formatted_similarity = f"According to OpenCLIP, the image and the text prompt are {clipped_similarity:.2f}% similar."
+
+    return formatted_similarity
+
+# Set up Gradio interface
+iface = gr.Interface(
+    fn=calculate_similarity,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image", height=512),
+        gr.Textbox(label="Text Prompt")
+    ],
+    outputs=gr.Text(),
+    allow_flagging="never",
+    title="OpenClip Cosine Similarity Calculator",
+    description="Provide a text prompt and upload an image to calculate the cosine similarity."
+)
+
+# Launch the interface with a public link for sharing online
+iface.launch(share=True)