Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import faiss
+import gradio as gr
+from sentence_transformers import SentenceTransformer
+
+# Load the CSV file with embeddings
+csv_path = 'df_after_rec_embedding.csv'  # Update with your actual CSV path
+df = pd.read_csv(csv_path)
+data = df.to_numpy().astype('float32')
+
+# Create a FAISS index
+dimension = data.shape[1]
+index = faiss.IndexFlatL2(dimension)  # L2 distance metric
+index.add(data)  # Add data to the index
+
+# Load the nomic-ai/nomic-embed-text-v1 model
+model = SentenceTransformer('nomic-ai/nomic-embed-text-v1',device='cpu', trust_remote_code=True)
+
+
+# Function to embed query and search using FAISS
+def search(query):
+    # Embed the query using the model
+    query_vector = model.encode([query])[0].astype('float32')
+
+    # Search the FAISS index
+    distances, indices = index.search(np.array([query_vector]), k=5)  # Search for top 5 closest vectors
+
+    # Return results with indices and distances
+    return [f"Index: {i}, Distance: {d:.4f}" for i, d in zip(indices[0], distances[0])]
+
+
+# Create the Gradio interface
+def gradio_app():
+    with gr.Blocks() as demo:
+        gr.Markdown("## FAISS Search Interface with Nomic Embedder")
+
+        with gr.Row():
+            with gr.Column():
+                query_input = gr.Textbox(
+                    label="Search Query",
+                    placeholder="Type your search query here"
+                )
+                search_button = gr.Button("Search")
+
+            with gr.Column():
+                search_results = gr.Textbox(label="Search Results")
+
+        search_button.click(
+            fn=search,
+            inputs=[query_input],
+            outputs=[search_results]
+        )
+
+    return demo
+
+
+# Launch the Gradio app
+demo = gradio_app()
+demo.launch()