Create app.py

app.py

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+import pandas as pd
+import gradio as gr
+import matplotlib.pyplot as plt
+import io
+import base64
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import linear_kernel
+
+# Load your dataset
+data = pd.read_csv('Blinkit Cart Prediction.csv')
+
+# Create a TF-IDF vectorizer
+tfidf_vectorizer = TfidfVectorizer(max_features=1000)  # Adjust max_features as needed
+tfidf_matrix = tfidf_vectorizer.fit_transform(data['Description'])
+
+# Function to recommend products
+def recommend_products(user_choice, num_recommendations=10):
+    # Transform the user choice using the same TF-IDF vectorizer
+    user_choice_vector = tfidf_vectorizer.transform([user_choice])
+
+    # Compute the cosine similarity between the user choice and all products
+    cosine_similarities = linear_kernel(user_choice_vector, tfidf_matrix)
+
+    # Get the indices of the most similar products
+    similar_indices = cosine_similarities.argsort()[0][-num_recommendations - 1:-1][::-1]
+
+    # Get the recommended product IDs and names
+    recommended_products = data.iloc[similar_indices][['ProductID', 'ProductName']]
+
+    return recommended_products
+
+# Define the input and output components
+input_component = gr.inputs.Textbox(label="Enter Your Choice")
+output_component = gr.outputs.HTML(label="Recommended Products")
+
+# Create the Gradio interface
+def recommend_interface(user_choice):
+    recommended_products = recommend_products(user_choice)
+    
+    # Create a bar graph of recommended products
+    plt.figure(figsize=(10, 6))
+    plt.bar(recommended_products['ProductName'], range(len(recommended_products)), color='skyblue')
+    plt.xticks(rotation=45, ha="right")
+    plt.xlabel("Recommended Products")
+    plt.ylabel("Ranking")
+    plt.title("Top Recommended Products")
+    
+    # Encode the image as base64
+    buffer = io.BytesIO()
+    plt.savefig(buffer, format="png")
+    graph_base64 = base64.b64encode(buffer.getvalue()).decode()
+    plt.close()
+    
+    # Create an HTML string with an embedded image
+    graph_html = f'<img src="data:image/png;base64,{graph_base64}" />'
+    
+    # Create an HTML string for the table of recommended products
+    table_html = recommended_products.to_html(index=False)
+    
+    # Concatenate the HTML strings for both components
+    result_html = f"<h2>Recommended Products:</h2>{table_html}<br>{graph_html}"
+    
+    return result_html
+
+# Launch the Gradio app
+interface = gr.Interface(
+fn=recommend_interface, inputs=input_component, outputs=output_component,
+            live = True,   
+    description = " Press flag if any erroneous output comes ",
+    theme=gr.themes.Soft(),
+    title = "Blinkit Cart Prediction",
+    examples = [['necklace'],
+                ['DSLR camera '],['tea '], ['Smart TV '] , ['protein bars'] , ['sunglasses ']  ],
+    )
+interface.launch(inline=False)