Duplicate from ajcdp/Zero-Shot-Classification-Pretrained

Co-authored-by: AJ L <[email protected]>

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README.md

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+---
+title: Zero Shot Classification Pretrained
+emoji: 🦀
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+sdk_version: 3.16.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: ajcdp/Zero-Shot-Classification-Pretrained
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from transformers import pipeline
+
+# Create the pipeline object
+pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32")
+
+# Define the function that will be used by the interface
+def zero_shot_classification(image, labels_text):
+    # Convert image to a PIL image object
+    pil_image = Image.fromarray(np.uint8(image)).convert("RGB")
+    
+    # Split the labels text into a list of labels
+    labels = labels_text.split(",")
+    
+    # Use the pipeline to classify the image with the given labels
+    res = pipe(
+        images=pil_image, 
+        candidate_labels=labels,
+        hypothesis_template= "This is a photo of a {}"
+    )
+    
+    # Return a dictionary mapping labels to scores
+    return {dic["label"]: dic["score"] for dic in res}
+    
+# Create the interface
+iface = gr.Interface(
+    zero_shot_classification, 
+    ["image", "text"], 
+    "label", 
+    examples=[
+        ["dog.jpg", "dog,cat,horse,zebra"],
+    ],
+    description="Add a picture and a list of labels separated by commas",
+    title="Zero-shot Image Classification"
+)
+
+# Launch the interface
+iface.launch()

requirements.txt

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+Pillow
+gradio
+numpy
+transformers
+torch