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Update app.py
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app.py
CHANGED
@@ -121,9 +121,14 @@ df = pd.read_csv("cleaned1.csv")
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df2 = pd.read_csv("cleaned2.csv")
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df3 = pd.read_csv("cleaned3.csv")
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embeddings = torch.load("
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embeddings2 = torch.load("
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embeddings3 = torch.load("
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# Pre-extract DataFrame columns to avoid repeated iloc calls
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df_questions = df["question"].values
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df_links = df["link"].values
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@@ -156,14 +161,7 @@ def predict(text):
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top3_scores3_cpu = top3_scores3.cpu().numpy()
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# Prepare results using pre-extracted arrays
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results = {
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{
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"question": df_questions[idx],
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"link": df_links[idx],
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"score": float(score)
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}
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for idx, score in zip(top3_idx1_cpu, top3_scores1_cpu)
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],
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"top2": [
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{
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"question": df2_questions[idx],
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@@ -180,6 +178,14 @@ def predict(text):
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}
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for idx, score in zip(top3_idx3_cpu, top3_scores3_cpu)
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],
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}
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return results
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df2 = pd.read_csv("cleaned2.csv")
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df3 = pd.read_csv("cleaned3.csv")
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embeddings = torch.load("embeddings1_1.pt")
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embeddings2 = torch.load("embeddings2_1.pt")
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embeddings3 = torch.load("embeddings3_1.pt")
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# embeddings = torch.load("embeddings1.pt")
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# embeddings2 = torch.load("embeddings2.pt")
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# embeddings3 = torch.load("embeddings3.pt")
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# Pre-extract DataFrame columns to avoid repeated iloc calls
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df_questions = df["question"].values
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df_links = df["link"].values
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top3_scores3_cpu = top3_scores3.cpu().numpy()
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# Prepare results using pre-extracted arrays
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results = {
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"top2": [
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{
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"question": df2_questions[idx],
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}
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for idx, score in zip(top3_idx3_cpu, top3_scores3_cpu)
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],
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"top1": [
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{
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"question": df_questions[idx],
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"link": df_links[idx],
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"score": float(score)
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}
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for idx, score in zip(top3_idx1_cpu, top3_scores1_cpu)
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],
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}
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return results
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