Update app.py

app.py

@@ -246,6 +246,8 @@ def record_participant(email, company, description, images):
     return pdf_file
 
 def create_pdf(images, description, email, company):
+    print(images)
+    
     filename = f"image_search_results_{email}.pdf"
     c = canvas.Canvas(filename, pagesize=letter)
     width, height = letter
@@ -280,6 +282,7 @@ def create_pdf(images, description, email, company):
         y_position -= 15  # Adjust for line spacing
 
     for image in images:
+        print(image)
         y_position -= 300  # Adjust this based on your image sizes
         if y_position <= 150:
             c.showPage()
@@ -367,6 +370,48 @@ def start_image_search(image, text):
 
 with gr.Blocks() as demo:
 
+        with gr.Tab("Celeb Matcher Demo"):
+            with gr.Tab("Demo"):
+                gr.Markdown("""
+                    # MongoDB's Vector Celeb Image Matcher  
+    
+                    Upload an image and find the most similar celeb image from the database, along with an AI-generated description.
+    
+                    💪 Make a great pose to impact the search! 🤯
+                    """)
+                with gr.Row():
+                    with gr.Column():
+                        image_input = gr.Image(type="pil", label="Upload an image")
+                        text_input = gr.Textbox(label="Enter an adjustment to the image")
+                        search_button = gr.Button("Search")
+                
+                    
+                    with gr.Column():
+                        output_gallery = gr.Gallery(label="Located images", show_label=False, elem_id="gallery",
+                                                    columns=[3], rows=[1], object_fit="contain", height="auto")
+                        output_description = gr.Textbox(label="AI Based vision description")
+                gr.Markdown("""
+    
+                        """)
+                with gr.Row():
+                    email_input = gr.Textbox(label="Enter your email")
+                    company_input = gr.Textbox(label="Enter your company name")
+                    record_button = gr.Button("Record & Download PDF")
+    
+                search_button.click(
+                    fn=start_image_search,
+                    inputs=[image_input, text_input],
+                    outputs=[output_gallery, output_description]
+                )
+    
+                record_button.click(
+                    fn=record_participant,
+                    inputs=[email_input, company_input, output_description, output_gallery],
+                    outputs=gr.File(label="Download Search Results as PDF")
+                )
+            with gr.Tab("Code"):
+                gr.Code(label="Code", language="python", value=fetch_url_data('https://huggingface.co/spaces/MongoDB/aws-bedrock-celeb-matcher/raw/main/app.py'))
+        
     with gr.Tab("Chat RAG Demo"):
         with gr.Tab("Demo"):
             gr.ChatInterface(get_movies, examples=["What movies are scary?", "Find me a comedy", "Movies for kids"], title="Movies Atlas Vector Search",description="This small chat uses a similarity search to find relevant movies, it uses MongoDB Atlas Vector Search read more here: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-tutorial",submit_btn="Search").queue()
@@ -408,48 +453,7 @@ with gr.Blocks() as demo:
         with gr.Tab("Code"):
              gr.Code(label="Code", language="python", value=fetch_url_data('https://huggingface.co/spaces/MongoDB/whatscooking-advisor/raw/main/app.py'))
 
-    with gr.Tab("Celeb Matcher Demo"):
-        with gr.Tab("Demo"):
-            gr.Markdown("""
-                # MongoDB's Vector Celeb Image Matcher  
-
-                Upload an image and find the most similar celeb image from the database, along with an AI-generated description.
-
-                💪 Make a great pose to impact the search! 🤯
-                """)
-            with gr.Row():
-                with gr.Column():
-                    image_input = gr.Image(type="pil", label="Upload an image")
-                    text_input = gr.Textbox(label="Enter an adjustment to the image")
-                    search_button = gr.Button("Search")
-            
-                
-                with gr.Column():
-                    output_gallery = gr.Gallery(label="Located images", show_label=False, elem_id="gallery",
-                                                columns=[3], rows=[1], object_fit="contain", height="auto")
-                    output_description = gr.Textbox(label="AI Based vision description")
-            gr.Markdown("""
-
-                    """)
-            with gr.Row():
-                email_input = gr.Textbox(label="Enter your email")
-                company_input = gr.Textbox(label="Enter your company name")
-                record_button = gr.Button("Record & Download PDF")
-
-            search_button.click(
-                fn=start_image_search,
-                inputs=[image_input, text_input],
-                outputs=[output_gallery, output_description]
-            )
 
-            record_button.click(
-                fn=record_participant,
-                inputs=[email_input, company_input, output_description, output_gallery],
-                outputs=gr.File(label="Download Search Results as PDF")
-            )
-        with gr.Tab("Code"):
-            gr.Code(label="Code", language="python", value=fetch_url_data('https://huggingface.co/spaces/MongoDB/aws-bedrock-celeb-matcher/raw/main/app.py'))
-    
    
 if __name__ == "__main__":
     demo.launch()