Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import faiss
+import numpy as np
+import os
+from sentence_transformers import SentenceTransformer
+
+# Load the pre-trained embedding model
+model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-1.5B-instruct", trust_remote_code=True)
+
+# Set the max sequence length
+model.max_seq_length = 512
+
+# Load the JSON data into a DataFrame
+df = pd.read_json('White-Stride-Red-68.json')
+df['embeding_context'] = df['embeding_context'].astype(str).fillna('')
+
+# Filter out any rows where 'embeding_context' might be empty or invalid
+df = df[df['embeding_context'] != '']
+    
+index = faiss.read_index('vector_store.index')
+
+
+# Function to perform search and return all columns
+def search_query(query_text):
+    num_records = 50
+
+    # Encode the input query text
+    embeddings_query = model.encode([query_text], prompt_name="query")
+    embeddings_query_np = np.array(embeddings_query).astype('float32')
+
+    # Search in FAISS index for nearest neighbors
+    distances, indices = index.search(embeddings_query_np, num_records)
+
+    # Get the top results based on FAISS indices
+    result_df = df.iloc[indices[0]].drop(columns=['embeding_context']).drop_duplicates().reset_index(drop=True)
+
+    return result_df
+
+# Gradio interface function
+def gradio_interface(query_text):
+    search_results = search_query(query_text)
+    return search_results
+
+# Gradio interface setup
+with gr.Blocks() as app:
+    gr.Markdown("<h1>White Stride Red Search (GTE-Qwen2)</h1>")
+    
+    # Input text box for the search query
+    search_input = gr.Textbox(label="Search Query", placeholder="Enter search text", interactive=True)
+    
+    # Output table for displaying results
+    search_output = gr.DataFrame(label="Search Results")
+    
+    # Search button
+    search_button = gr.Button("Search")
+    
+    # Link button click to action
+    search_button.click(fn=gradio_interface, inputs=search_input, outputs=search_output)
+
+# Launch the Gradio app
+app.launch()
+