wew

app.py

@@ -1,49 +1,16 @@
 import gradio as gr
-from transformers import pipeline
-from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, roc_curve, auc
-import matplotlib.pyplot as plt
-import numpy as np
+from PIL import Image
 
-# Initialize the sentiment analysis pipeline with a multilingual model
-sentiment_analysis = pipeline("sentiment-analysis", model="bert-base-multilingual-cased")
+def to_black_and_white(input_image):
+    bw_image = input_image.convert("L")  # Convert to grayscale
+    return bw_image
 
-def analyze_sentiment(text):
-    result = sentiment_analysis(text)
-    return result[0]
+with gr.Blocks() as app:
+    gr.Markdown("### Black and White Image Maker")
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload your Image")
+        image_output = gr.Image(type="pil", label="Black and White Image", tool="editor")
 
-# Mock functions to calculate metrics - Replace with actual implementation
-def calculate_metrics(y_true, y_pred):
-    accuracy = accuracy_score(y_true, y_pred)
-    precision, recall, f1, _ = precision_recall_fscore_support(y_true, y_pred, average='binary')
-    cm = confusion_matrix(y_true, y_pred)
-    fpr, tpr, _ = roc_curve(y_true, y_pred)
-    roc_auc = auc(fpr, tpr)
-    return accuracy, precision, recall, f1, cm, fpr, tpr, roc_auc
+    image_input.change(to_black_and_white, inputs=image_input, outputs=image_output)
 
-def plot_confusion_matrix(cm):
-    # Plot confusion matrix here
-    pass
-
-def plot_roc_curve(fpr, tpr, roc_auc):
-    # Plot ROC curve here
-    pass
-
-# Replace this with actual test data and predictions
-y_true = [0, 1, 0, 1]  # True labels
-y_pred = [0, 1, 0, 1]  # Predicted labels
-
-# Calculate metrics
-accuracy, precision, recall, f1, cm, fpr, tpr, roc_auc = calculate_metrics(y_true, y_pred)
-
-# Plot confusion matrix and ROC curve
-plot_confusion_matrix(cm)
-plot_roc_curve(fpr, tpr, roc_auc)
-
-# Create a Gradio interface
-interface = gr.Interface(
-    fn=analyze_sentiment,
-    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter Text Here..."),
-    outputs="text"
-)
-
-interface.launch()
+app.launch()

requirements.txt

@@ -1,5 +1,2 @@
-transformers==4.36.2
-gradio==4.14.0
-scikit-learn
-matplotlib
-tensorflow
+gradio
+PIL