Update app.py

app.py

@@ -1,51 +1,43 @@
 import pandas as pd
-import numpy as np
-from sentence_transformers import SentenceTransformer
-import faiss
 import gradio as gr
-from datasets import load_dataset
+from sentence_transformers import SentenceTransformer, util
 
-# 1) Load from Hugging Face dataset
-ds = load_dataset("Abirate/english_quotes", split="train")
-quotes = [q["quote"] for q in ds.select(range(1000))]
-df = pd.DataFrame({"text": quotes})
+# Load dataset
+df = pd.read_csv("quotes.csv")
+quotes = df["quote"].tolist()
 
-# 2) Embeddings + FAISS setup
+# Load embedding model
 model = SentenceTransformer("all-MiniLM-L6-v2")
-embeddings = model.encode(df["text"].tolist(), convert_to_numpy=True).astype("float32")
-faiss.normalize_L2(embeddings)
-index = faiss.IndexFlatIP(embeddings.shape[1])
-index.add(embeddings)
-
-# 3) Query function
-def get_top3(query):
-    qv = model.encode([query], convert_to_numpy=True).astype("float32")
-    faiss.normalize_L2(qv)
-    D, I = index.search(qv, 3)
-    return "\n\n".join(f"{i+1}. {df.iloc[idx]['text']}" for i, idx in enumerate(I[0]))
-
-# 4) Gradio UI
+quote_embeddings = model.encode(quotes, convert_to_tensor=True)
+
+# Function to find top 3 similar quotes
+def get_top3(user_input):
+    if not user_input.strip():
+        return "Please enter a search phrase."
+    
+    input_embedding = model.encode(user_input, convert_to_tensor=True)
+    similarities = util.pytorch_cos_sim(input_embedding, quote_embeddings)[0]
+    top_results = similarities.argsort(descending=True)[:3]
+
+    output_quotes = []
+    for idx in top_results:
+        output_quotes.append(f"- {quotes[idx]}")
+    
+    return "\n".join(output_quotes)
+
+# Create Gradio interface
 demo = gr.Interface(
     fn=get_top3,
     inputs=gr.Textbox(lines=2, placeholder="Type something..."),
     outputs="text",
     title="Quote Finder (1,000+ quotes)",
-    description="Enter any sentence to find 3 semantically similar quotes."
-)
-
-if __name__ == "__main__":
-   demo = gr.Interface(
-    fn=get_top3,
-    inputs=gr.Textbox(lines=2, placeholder="Type something..."),
-    outputs="text",
-    title="Quote Finder (1,000+ quotes)",
-    description="Enter any sentence to find 3 semantically similar quotes.",
+    description="Enter any topic or phrase to find 3 semantically similar quotes from our dataset.",
     examples=[
         ["happiness"],
-        ["overcoming fear"],
-        ["success in life"]
+        ["overcoming failure"],
+        ["friendship"]
     ]
 )
 
-
-
+if __name__ == "__main__":
+    demo.launch()