Update app.py

app.py

@@ -3,6 +3,9 @@ import transformers
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModel
 import torch
 import torch.nn as nn
+import matplotlib.pyplot as plt
+import pandas as pd
+
 
 
 class LogisticRegressionTorch(nn.Module):
@@ -110,9 +113,15 @@ def analyze_dna(sequence):
     top_5_labels = [int_to_label[i] for i in top_5_indices]
     
     # Prepare the output as a list of tuples (label_name, probability)
-    result = [(label, prob) for label, prob in zip(top_5_labels, top_5_probs)]
-    
-    return result
+    #result = [(label, prob) for label, prob in zip(top_5_labels, top_5_probs)]
+    # Plot histogram
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.barh(top_5_labels, top_5_probs, color='skyblue')
+    ax.set_xlabel('Probability')
+    ax.set_title('Top 5 Most Likely Labels')
+    plt.gca().invert_yaxis()  # Highest probabilities at the top
+
+    #return result
 
 # Create a Gradio interface
 demo = gr.Interface(fn=analyze_dna, inputs="text", outputs="json")