Create app.py

app.py

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+import gradio as gr
+import re
+
+# -----------------------------------------
+# GLOBALS for Bag-of-Words ML Simulation
+# -----------------------------------------
+positive_word_counts = {}
+negative_word_counts = {}
+training_data = []
+
+# -----------------------------------------
+# 1) Training Function
+# -----------------------------------------
+def train_model(statement, label):
+    """
+    Splits a statement into words, increments counts in
+    positive_word_counts or negative_word_counts depending on label.
+    Returns HTML feedback showing what's been learned.
+    """
+    global positive_word_counts, negative_word_counts, training_data
+
+    # Basic error check
+    statement = statement.strip()
+    if not statement:
+        return "<p style='color:red;'>Please enter a valid training statement.</p>"
+
+    # Tokenize by letters only
+    words = re.findall(r"[a-zA-Z]+", statement.lower())
+
+    # Update dictionary
+    if label == "Positive":
+        for w in words:
+            positive_word_counts[w] = positive_word_counts.get(w, 0) + 1
+    else:
+        for w in words:
+            negative_word_counts[w] = negative_word_counts.get(w, 0) + 1
+
+    training_data.append((statement, label))
+
+    # Construct feedback
+    pos_count = len(positive_word_counts)
+    neg_count = len(negative_word_counts)
+    response = (
+        f"<p><b>Trained:</b> '{statement}' as {label}</p>"
+        f"<p>Learned {pos_count} unique positive words "
+        f"and {neg_count} unique negative words so far.</p>"
+    )
+    return response
+
+# -----------------------------------------
+# 2) Classification Function
+# -----------------------------------------
+def classify_text(statement):
+    """
+    Counts how many times each word appears in positive vs. negative.
+    Returns the classification result + an explanation.
+    """
+    statement = statement.strip()
+    if not statement:
+        return "<p style='color:red;'>Please enter a statement to classify.</p>"
+
+    words = re.findall(r"[a-zA-Z]+", statement.lower())
+    pos_score = 0
+    neg_score = 0
+
+    # Accumulate scores
+    for w in words:
+        pos_score += positive_word_counts.get(w, 0)
+        neg_score += negative_word_counts.get(w, 0)
+
+    # Decide label
+    label = "Positive" if pos_score >= neg_score else "Negative"
+
+    explanation = f"""
+    <p><b>Classification:</b> '{statement}' → {label}</p>
+    <p>Pos score: {pos_score}, Neg score: {neg_score}</p>
+    <h4>How This Simulated ML Works</h4>
+    <ul>
+      <li>When you train a statement, each word is counted as either positive or negative.</li>
+      <li>When classifying, we sum how many times those words appeared in positive vs. negative examples.</li>
+      <li>If there are more 'positive' occurrences, we predict Positive; otherwise Negative.</li>
+    </ul>
+    <p>This is a basic 'Bag-of-Words' approach. Real ML uses more sophisticated methods and bigger datasets.</p>
+    """
+    return explanation
+
+# -----------------------------------------
+# 3) Gradio Interface
+# -----------------------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## Simulated Machine Learning (Bag-of-Words) Demo")
+    gr.Markdown("Train a simple word-count-based model, then classify new statements.")
+
+    gr.Markdown("### Training Section")
+    with gr.Row():
+        train_statement_input = gr.Textbox(
+            label="Training Statement",
+            placeholder="e.g. I love this place"
+        )
+        train_label_dropdown = gr.Dropdown(
+            choices=["Positive", "Negative"],
+            value="Positive",
+            label="Label"
+        )
+        train_button = gr.Button("Train Statement")
+    train_output = gr.HTML()
+
+    train_button.click(
+        fn=train_model,
+        inputs=[train_statement_input, train_label_dropdown],
+        outputs=train_output
+    )
+
+    gr.Markdown("### Classification Section")
+    with gr.Row():
+        classify_statement_input = gr.Textbox(
+            label="Test Statement",
+            placeholder="e.g. I really love these tacos"
+        )
+        classify_button = gr.Button("Classify")
+    classify_output = gr.HTML()
+
+    classify_button.click(
+        fn=classify_text,
+        inputs=[classify_statement_input],
+        outputs=classify_output
+    )
+
+demo.launch()