Update app.py

app.py

@@ -1,105 +1,108 @@
-import pandas as pd
-from sentence_transformers import SentenceTransformer, util
-from transformers import pipeline
-import torch
-import gradio as gr
-
-# Load the dataset
-df = pd.read_csv(r"C:\Users\Saarthak\Desktop\Saarthak_assignment\analytics_vidhya_data.csv", encoding='ISO-8859-1')
-
-# Load the pre-trained model for embeddings (using SentenceTransformers)
-model = SentenceTransformer('multi-qa-mpnet-base-dot-v1')
-
-# Combine title and description to create a full text for each course
-df['full_text'] = df.iloc[:,0] + " " + df.iloc[:,1] + " " + df['Instructor Name'] + " " + str(df['Rating']) + " " + df['Category']
-
-# Convert full course texts into embeddings
-course_embeddings = model.encode(df['full_text'].tolist(), convert_to_tensor=True)
-
-# Function to expand the query using paraphrasing
-def expand_query(query):
-    paraphraser = pipeline('text2text-generation', model='Vamsi/T5_Paraphrase_Paws')
-    expanded_queries = paraphraser(query, num_return_sequences=3, max_length=50, do_sample=True)
-    return [q['generated_text'] for q in expanded_queries]
-
-# Function to search for the most relevant courses
-def search_courses(query, level_filter=None, category_filter=None, top_k=3):
-    # Step 1: Expand the query using paraphrasing
-    expanded_queries = expand_query(query)
-
-    # Step 2: Initialize an array to store all similarities
-    all_similarities = []
-
-    for expanded_query in expanded_queries:
-        # Convert each expanded query into an embedding
-        query_embedding = model.encode(expanded_query, convert_to_tensor=True)
-
-        # Compute cosine similarities between the query embedding and course embeddings
-        similarities = util.pytorch_cos_sim(query_embedding, course_embeddings)[0]
-
-        # Append to the list of all similarities
-        all_similarities.append(similarities)
-
-    # Step 3: Convert the list of tensors to a single tensor by taking the maximum similarity for each course
-    aggregated_similarities = torch.max(torch.stack(all_similarities), dim=0)[0]
-
-    # Step 4: Apply filters
-    filtered_df = df.copy()
-    if level_filter:
-        filtered_df = filtered_df[filtered_df['Level of Difficulty'] == level_filter]
-    if category_filter:
-        filtered_df = filtered_df[filtered_df['Category'] == category_filter]
-    
-    if filtered_df.empty:
-        return "<p>No matching courses found.</p>"
-    
-    # Recalculate similarities for the filtered data
-    filtered_similarities = aggregated_similarities[filtered_df.index]
-
-    # Step 5: Get top_k most similar courses
-    top_results = filtered_similarities.topk(k=min(top_k, len(filtered_similarities)))
-
-    # Prepare the output as clickable links
-    results = []
-    for idx in top_results.indices:
-        idx = int(idx)
-        course_title = filtered_df.iloc[idx]['Course Title']
-        course_description = filtered_df.iloc[idx,1]
-        course_url = filtered_df.iloc[idx,-1]
-        
-        
-        # Format the result as a clickable hyperlink using raw HTML
-        course_link = f'<a href="{course_url}" target="_blank">{course_title}</a>'
-        results.append(f"<strong>{course_link}</strong><br>{course_description}<br><br>")
-
-    # Combine all results into an HTML formatted list
-    return "<ol>" + "".join([f"<li>{result}</li>" for result in results]) + "</ol>"
-
-# Create Gradio UI
-def create_gradio_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown("# 📚 Analytics Vidhya Free Courses")
-        gr.Markdown("Enter your query and use filters to narrow down the search.")
-
-        # Input elements
-        query = gr.Textbox(label="🔍 Search for a course", placeholder="Enter course topic or description")
-
-        # Filters (in a collapsible form)
-        with gr.Accordion("🔍 Filters", open=False):
-            level_filter = gr.Dropdown(choices=["Beginner", "Intermediate", "Advanced"], label="📚 Course Level", multiselect=False)
-            category_filter = gr.Dropdown(choices=["Data Science", "Machine Learning", "Deep Learning", "AI", "NLP"], label="📂 Category", multiselect=False)
-
-        # Search button
-        search_button = gr.Button("Search")
-
-        # Output HTML for displaying results
-        output = gr.HTML(label="Search Results")
-
-        # On button click, trigger the search function
-        search_button.click(fn=search_courses, inputs=[query, level_filter, category_filter], outputs=output)
-
-    return demo
-
-# Launch Gradio interface
-demo = create_gradio_interface()
-demo.launch(share=True, debug=True)
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+from transformers import pipeline
+import torch
+import gradio as gr
+
+# Use the relative path where the CSV is uploaded
+csv_file_path = os.path.join(os.getcwd(), 'analytics_vidhya_data.csv')
+
+# Load the dataset
+df = pd.read_csv(csv_file_path, encoding='ISO-8859-1')
+
+# Load the pre-trained model for embeddings (using SentenceTransformers)
+model = SentenceTransformer('multi-qa-mpnet-base-dot-v1')
+
+# Combine title and description to create a full text for each course
+df['full_text'] = df.iloc[:,0] + " " + df.iloc[:,1] + " " + df['Instructor Name'] + " " + str(df['Rating']) + " " + df['Category']
+
+# Convert full course texts into embeddings
+course_embeddings = model.encode(df['full_text'].tolist(), convert_to_tensor=True)
+
+# Function to expand the query using paraphrasing
+def expand_query(query):
+    paraphraser = pipeline('text2text-generation', model='Vamsi/T5_Paraphrase_Paws')
+    expanded_queries = paraphraser(query, num_return_sequences=3, max_length=50, do_sample=True)
+    return [q['generated_text'] for q in expanded_queries]
+
+# Function to search for the most relevant courses
+def search_courses(query, level_filter=None, category_filter=None, top_k=3):
+    # Step 1: Expand the query using paraphrasing
+    expanded_queries = expand_query(query)
+
+    # Step 2: Initialize an array to store all similarities
+    all_similarities = []
+
+    for expanded_query in expanded_queries:
+        # Convert each expanded query into an embedding
+        query_embedding = model.encode(expanded_query, convert_to_tensor=True)
+
+        # Compute cosine similarities between the query embedding and course embeddings
+        similarities = util.pytorch_cos_sim(query_embedding, course_embeddings)[0]
+
+        # Append to the list of all similarities
+        all_similarities.append(similarities)
+
+    # Step 3: Convert the list of tensors to a single tensor by taking the maximum similarity for each course
+    aggregated_similarities = torch.max(torch.stack(all_similarities), dim=0)[0]
+
+    # Step 4: Apply filters
+    filtered_df = df.copy()
+    if level_filter:
+        filtered_df = filtered_df[filtered_df['Level of Difficulty'] == level_filter]
+    if category_filter:
+        filtered_df = filtered_df[filtered_df['Category'] == category_filter]
+    
+    if filtered_df.empty:
+        return "<p>No matching courses found.</p>"
+    
+    # Recalculate similarities for the filtered data
+    filtered_similarities = aggregated_similarities[filtered_df.index]
+
+    # Step 5: Get top_k most similar courses
+    top_results = filtered_similarities.topk(k=min(top_k, len(filtered_similarities)))
+
+    # Prepare the output as clickable links
+    results = []
+    for idx in top_results.indices:
+        idx = int(idx)
+        course_title = filtered_df.iloc[idx]['Course Title']
+        course_description = filtered_df.iloc[idx,1]
+        course_url = filtered_df.iloc[idx,-1]
+        
+        
+        # Format the result as a clickable hyperlink using raw HTML
+        course_link = f'<a href="{course_url}" target="_blank">{course_title}</a>'
+        results.append(f"<strong>{course_link}</strong><br>{course_description}<br><br>")
+
+    # Combine all results into an HTML formatted list
+    return "<ol>" + "".join([f"<li>{result}</li>" for result in results]) + "</ol>"
+
+# Create Gradio UI
+def create_gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# 📚 Analytics Vidhya Free Courses")
+        gr.Markdown("Enter your query and use filters to narrow down the search.")
+
+        # Input elements
+        query = gr.Textbox(label="🔍 Search for a course", placeholder="Enter course topic or description")
+
+        # Filters (in a collapsible form)
+        with gr.Accordion("🔍 Filters", open=False):
+            level_filter = gr.Dropdown(choices=["Beginner", "Intermediate", "Advanced"], label="📚 Course Level", multiselect=False)
+            category_filter = gr.Dropdown(choices=["Data Science", "Machine Learning", "Deep Learning", "AI", "NLP"], label="📂 Category", multiselect=False)
+
+        # Search button
+        search_button = gr.Button("Search")
+
+        # Output HTML for displaying results
+        output = gr.HTML(label="Search Results")
+
+        # On button click, trigger the search function
+        search_button.click(fn=search_courses, inputs=[query, level_filter, category_filter], outputs=output)
+
+    return demo
+
+# Launch Gradio interface
+demo = create_gradio_interface()
+demo.launch(share=True, debug=True)