import matplotlib.pyplot as plt
import numpy as np

# Define approaches and challenges/research gaps
approaches = [
    "Conventional Methods", 
    "Modeling with ANNs", 
    "Machine Learning Applications", 
    "Vision-based Detection", 
    "Real-world Implementations"
]
challenges = [
    "Scalability & Applicability", 
    "Data Quality & Scarcity", 
    "Interpretability & Explainability", 
    "Environmental Conditions", 
    "Cost & Scalability", 
    "User Adoption"
]

# Research gap severity matrix (1 = low, 5 = high)
research_gap_matrix = np.array([
    [3, 2, 4, 3, 2, 2],   # Conventional Methods
    [4, 5, 5, 4, 3, 2],   # Modeling with ANNs
    [3, 4, 5, 3, 2, 4],   # Machine Learning Applications
    [2, 4, 3, 5, 3, 2],   # Vision-based Detection
    [4, 3, 2, 3, 5, 4]    # Real-world Implementations
])

# Create the heatmap using matplotlib
fig, ax = plt.subplots(figsize=(10, 6))

# Create a color map based on the values in the research_gap_matrix
cax = ax.imshow(research_gap_matrix, cmap="Blues", aspect='auto')

# Add a color bar (linked to 'cax' which is a ScalarMappable)
fig.colorbar(cax)

# Set axis labels and ticks
ax.set_xticks(np.arange(len(challenges)))
ax.set_yticks(np.arange(len(approaches)))
ax.set_xticklabels(challenges, rotation=45, ha='left')
ax.set_yticklabels(approaches)

# Annotate each cell with its value
for i in range(len(approaches)):
    for j in range(len(challenges)):
        ax.text(j, i, research_gap_matrix[i, j], va='center', ha='center', color="black")

# Add title and adjust layout
plt.title("Research Gap Matrix: Approaches vs Challenges", pad=20)
plt.tight_layout()

# Save the heatmap as an image file
plt.savefig('foo.png', dpi=300)

# Show the heatmap
plt.show()