Update app.py

app.py

@@ -3,6 +3,8 @@ import torch
 import clip
 from PIL import Image
 import numpy as np
+from pytrends.request import TrendReq
+import matplotlib.pyplot as plt
 
 # Load the CLIP model
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -17,9 +19,27 @@ with torch.no_grad():
     category_embeddings = model.encode_text(clip.tokenize(categories).to(device))
     attribute_embeddings = model.encode_text(clip.tokenize(attributes).to(device))
 
+def plot_trends(dataframe):
+    plt.figure(figsize=(12,6))
+    for column in dataframe.columns:
+        if column != 'isPartial':
+            plt.plot(dataframe.index, dataframe[column], label=column)
+    plt.legend()
+    plt.title("Google Trends Over Time")
+    plt.xlabel("Time")
+    plt.ylabel("Interest")
+    plt.grid(True)
+    plt.tight_layout()
+
+    # Save the plot to a temporary file and return its path
+    path = "trends_plot.png"
+    plt.savefig(path)
+    plt.close()
+    return path
+    
 def predict_apparel_and_attributes(image):
-    pil_image = Image.fromarray((image * 255).astype(np.uint8))
-    image_input = preprocess(pil_image).unsqueeze(0).to(device)
+    #pil_image = Image.fromarray((image * 255).astype(np.uint8))
+    image_input = preprocess(image).unsqueeze(0).to(device)
     
     with torch.no_grad():
         image_embedding = model.encode_image(image_input)
@@ -32,10 +52,30 @@ def predict_apparel_and_attributes(image):
     top_category = categories[category_similarities.argmax().item()]
     top_attributes = [attributes[i] for i in attribute_similarities.argsort(descending=True)[:3]]  # top 3 attributes
     print(f"results:{top_category, ','.join(top_attributes)}")
-    return top_category, ", ".join(top_attributes)
 
-# Define Gradio interface
-demo = gr.Interface(fn=predict_apparel_and_attributes, inputs=gr.Image(label="Upload an apparel image"),outputs="text")
-    
+    # Fetch trends for the top apparel category and attributes
+    pytrend = TrendReq()
+    keywords = [top_category] + top_attributes
+    pytrend.build_payload(kw_list=keywords, timeframe='now 1-H', geo='', gprop='')
+    interest_over_time_df = pytrend.interest_over_time()
+
+    # Plot the trends and get the path to the saved plot
+    plot_path = plot_trends(interest_over_time_df)
+
+    #trends_text = interest_over_time_df.to_string()
+
+    return top_category, ", ".join(top_attributes), plot_path
+
+demo = gr.Interface(
+    predict_apparel_and_attributes,
+    gr.Image(type="pil"),
+    outputs=[ gr.Textbox(label="Apparel Category"), 
+              gr.Textbox(label="Apparel Attributes"),
+              gr.Image(label="Google Trends Plot")],  # Output types
+    examples=[
+        os.path.join(os.path.abspath(''), "images/jeans.jpeg")
+    ],
+)
+
 if __name__ == "__main__":
-    demo.launch()   
+    demo.launch(debug=True)