SantanuBanerjee commited on
Commit
960359c
·
verified ·
1 Parent(s): f64fef3

Update app.py

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Files changed (1) hide show
  1. app.py +6 -4
app.py CHANGED
@@ -264,7 +264,7 @@ def extract_problem_domains(df,
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  # Perform K-Means clustering with Silhouette Analysis
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  silhouette_scores = []
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  for n_clusters in range(cluster_range[0], cluster_range[1] + 1):
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- kmeans = KMeans(n_clusters=n_clusters, random_state=42)
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  cluster_labels = kmeans.fit_predict(X)
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  silhouette_avg = silhouette_score(X, cluster_labels)
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  silhouette_scores.append(silhouette_avg)
@@ -293,9 +293,11 @@ def extract_problem_domains(df,
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  cluster_representations = {}
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  for i in range(optimal_n_clusters):
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  center = kmeans.cluster_centers_[i]
 
 
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  # top_word_indices = center.argsort()[-top_words:][::-1]
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- top_word_indices = center.argsort()[-top_words:][::-1].tolist()
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  top_words = [feature_names[index] for index in top_word_indices]
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  cluster_representations[i] = top_words
@@ -356,7 +358,7 @@ def nlp_pipeline(original_df, console_messages):
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  def process_excel(file):
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  console_messages = []
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- console_messages.append("Processing...")
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  try:
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  # Ensure the file path is correct
@@ -416,7 +418,7 @@ interface = gr.Interface(
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  # gr.File(label="Download the processed Excel File containing the ** Project Proposals ** for each Location~Problem paired combination"), # File download output
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  # gr.Textbox(label="Console Messages", lines=10, interactive=False) # Console messages output
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  # ],
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- # Use either below or above format
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  outputs=[
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  gr.Textbox(label="Console Messages", lines=25, interactive=False), # Console messages output
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  gr.File(label="Download the processed Excel File containing the ** Project Proposals ** for each Location~Problem paired combination") # File download output
 
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  # Perform K-Means clustering with Silhouette Analysis
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  silhouette_scores = []
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  for n_clusters in range(cluster_range[0], cluster_range[1] + 1):
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+ kmeans = KMeans(n_clusters=n_clusters)#, random_state=42)
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  cluster_labels = kmeans.fit_predict(X)
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  silhouette_avg = silhouette_score(X, cluster_labels)
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  silhouette_scores.append(silhouette_avg)
 
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  cluster_representations = {}
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  for i in range(optimal_n_clusters):
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  center = kmeans.cluster_centers_[i]
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+
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+
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  # top_word_indices = center.argsort()[-top_words:][::-1]
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+ top_word_indices = center.argsort()[-top_words:][::-1].tolist() # Indexes of top words
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  top_words = [feature_names[index] for index in top_word_indices]
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  cluster_representations[i] = top_words
 
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  def process_excel(file):
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  console_messages = []
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+ console_messages.append("Processing starts...")
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  try:
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  # Ensure the file path is correct
 
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  # gr.File(label="Download the processed Excel File containing the ** Project Proposals ** for each Location~Problem paired combination"), # File download output
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  # gr.Textbox(label="Console Messages", lines=10, interactive=False) # Console messages output
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  # ],
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+ Use either below or above format
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  outputs=[
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  gr.Textbox(label="Console Messages", lines=25, interactive=False), # Console messages output
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  gr.File(label="Download the processed Excel File containing the ** Project Proposals ** for each Location~Problem paired combination") # File download output