Update app.py

app.py

@@ -6,29 +6,31 @@ from sentence_transformers import SentenceTransformer
 # Model
 model = SentenceTransformer("all-MiniLM-L6-v2")
 
-# Global state for reusability
-supplier_df = None
-supplier_embeddings = None
-
-def load_and_process_supplier_data(file):
+# Load and process supplier data on startup
+def load_supplier_data():
     global supplier_df, supplier_embeddings
 
-    supplier_df = pd.read_excel(file)
+    supplier_df = pd.read_excel("SupplierList.xlsx")
     supplier_df["Capability"] = supplier_df["Capability"].fillna("")
     texts = supplier_df["Capability"].tolist()
 
-    # Compute embeddings
+    # Compute and normalize embeddings
     supplier_embeddings = model.encode(texts, convert_to_tensor=True, normalize_embeddings=True)
 
-    return f"{len(supplier_df)} suppliers loaded and processed."
+# Initial data load
+supplier_df = None
+supplier_embeddings = None
+load_supplier_data()
 
+# View loaded data
 def view_supplier_data():
     return supplier_df if supplier_df is not None else pd.DataFrame()
 
+# Match function
 def match_suppliers(event_description, top_k=5):
     if supplier_embeddings is None:
-        return "Please upload and process supplier data first."
-    
+        return "Supplier data not loaded."
+
     event_embedding = model.encode(event_description, convert_to_tensor=True, normalize_embeddings=True).unsqueeze(0)
     scores = torch.nn.functional.cosine_similarity(event_embedding, supplier_embeddings)
     top_indices = torch.topk(scores, k=top_k).indices.tolist()
@@ -43,21 +45,16 @@ def match_suppliers(event_description, top_k=5):
 
     return pd.DataFrame(results)
 
+# Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("## Supplier Matching App with Full Pipeline")
-
-    with gr.Tab("1. Upload Supplier Data"):
-        file_input = gr.File(label="Upload Excel file", file_types=[".xlsx"])
-        load_button = gr.Button("Load and Process")
-        load_output = gr.Textbox(label="Status")
-        load_button.click(load_and_process_supplier_data, inputs=file_input, outputs=load_output)
+    gr.Markdown("## Supplier Matching App")
 
-    with gr.Tab("2. View Supplier Data"):
-        view_button = gr.Button("Show Data")
+    with gr.Tab("1. View Supplier Data"):
+        view_button = gr.Button("Show Supplier Data")
         data_output = gr.Dataframe()
         view_button.click(view_supplier_data, outputs=data_output)
 
-    with gr.Tab("3. Match Use Case to Suppliers"):
+    with gr.Tab("2. Match Use Case to Suppliers"):
         input_text = gr.Textbox(lines=3, label="Describe Your Event/Use Case")
         top_k_input = gr.Slider(minimum=1, maximum=10, value=5, label="Top K Matches")
         match_button = gr.Button("Match Suppliers")