Delete da.py

da.py

@@ -1,61 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Da.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1Kg9-C_Fif3yO8FuXT84Tci-3ItuLsS7p
-"""
-"This is for running on google colab"
-
-#Library install
-!pip install transformers sentence-transformers gradio
-
-import pandas as pd
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
-import gradio as gr
-
-# Load the dataset
-df = pd.read_csv('/content/courses.csv')  # Replace with actual path to courses.csv
-
-# Load a pre-trained sentence transformer model
-model = SentenceTransformer('all-MiniLM-L6-v2')
-
-# Create a combined column for embedding (e.g., title + description + keywords)
-df['combined_text'] = df['title'] + " " + df['description'] + " " + df['keywords']
-course_embeddings = model.encode(df['combined_text'].tolist(), convert_to_tensor=True)
-
-def search_courses(user_query):
-    # Encode the user query
-    query_embedding = model.encode(user_query, convert_to_tensor=True)
-
-    # Compute cosine similarities between the query and each course embedding
-    similarities = cosine_similarity(
-        query_embedding.cpu().detach().numpy().reshape(1, -1),
-        course_embeddings.cpu().detach().numpy()
-    )
-
-    # Get indices of top matching courses (top 5 results)
-    top_matches = similarities.argsort()[0][-5:][::-1]
-
-    # Retrieve top matching courses
-    results = [{"title": df.iloc[i]["title"], "description": df.iloc[i]["description"]} for i in top_matches]
-    return results
-
-# Define Gradio function for user interaction
-def gradio_search(query):
-    results = search_courses(query)
-    return results
-
-# Set up Gradio interface
-iface = gr.Interface(
-    fn=gradio_search,
-    inputs="text",
-    outputs="json",
-    title="Smart Course Search",
-    description="Find the most relevant courses based on your query."
-)
-
-# Launch the app (for local testing or deploying in Hugging Face Spaces)
-iface.launch()