Create app.py

app.py

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+import assignment23
+from assignment23 import make_train_valid_dfs 
+from assignment23 import get_image_embeddings 
+from assignment23 import inference_CLIP
+
+import gradio as gr
+import zipfile
+import os
+import pandas as pd
+import subprocess
+
+
+image_path = "./Images"
+captions_path = "."
+data_source = 'flickr8k.zip'
+
+with zipfile.ZipFile(data_source, 'r') as zip_ref:
+    zip_ref.extractall('.')
+
+
+cmd = "pwd"
+output1 = subprocess.check_output(cmd, shell=True).decode("utf-8")
+
+
+
+cmd = "ls -l"
+output1 = subprocess.check_output(cmd, shell=True).decode("utf-8")
+
+
+
+
+df = pd.read_csv("captions.txt")
+df['id'] = [id_ for id_ in range(df.shape[0] // 5) for _ in range(5)]
+df.to_csv("captions.csv", index=False)
+df = pd.read_csv("captions.csv")
+
+
+_, valid_df = make_train_valid_dfs()
+
+model, image_embeddings = get_image_embeddings(valid_df, "best.pt")
+
+
+examples = ["man and women on road"]
+
+def greet(query_text):
+    print("Going to invoke inference_CLIP")
+    return inference_CLIP(query_text)
+    
+gallery = gr.Gallery(
+           label="CLIP result images", show_label=True, elem_id="gallery", 
+           columns=[3], rows=[3], object_fit="contain", height="auto")
+
+demo = gr.Interface(fn=greet, 
+                    inputs=gr.Dropdown(choices=examples, label="Search Image by text prompt"), 
+                    outputs=gallery,
+                    title="Open AI CLIP")
+
+
+demo.launch()