Merge branch 'main' of https://github.com/ANGELOANTU7/intel-hack

Author: heyronjmilton

Backend/Final_LatestSorter.py

@@ -63,7 +63,7 @@ def process_file(file_name,user1):
         # Read file from S3
         print(user1)
         response = s3.get_object(Bucket='learnmateai', Key=user1+'pyqs_txt/' + file_name)
-        file_content = response['Body'].read().decode('utf-16-le')
+        file_content = response['Body'].read().decode('utf-8')
 
         # Split file content into batches (adjust batch size as needed)
         batch_size = 30000

Backend/test/old_sorter.py renamed to Backend/Intel-oneAPI/old_sorter.py

@@ -0,0 +1,7 @@
+                             
+{'best_video_url': 'https://www.youtube.com/watch?v=GwIo3gDZCVQ', 'elapsed_time': 58.66309332847595}
+
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+{'best_video_url': 'https://www.youtube.com/watch?v=GwIo3gDZCVQ', 'elapsed_time': 54.04244136810303}
+
+

Backend/test/summariser.py renamed to Backend/Intel-oneAPI/summariser.py

@@ -0,0 +1,74 @@
+from sklearnex import unpatch_sklearn
+unpatch_sklearn()
+from sklearn.metrics.pairwise import cosine_similarity
+from transformers import pipeline
+import requests
+import json
+from youtube_transcript_api import YouTubeTranscriptApi
+from sentence_transformers import SentenceTransformer
+import torch
+import time
+
+# Load the Universal Sentence Encoder model
+model = SentenceTransformer('bert-base-nli-mean-tokens')
+
+# YouTube API parameters
+API_KEY = "AIzaSyAMD4FgbCjmp-_8g8nams4tsno4DV1mDnE"
+MAX_RESULTS = 50  # Maximum number of search results to retrieve
+
+# Search for videos using the YouTube API
+def search_videos(query):
+    url = f"https://www.googleapis.com/youtube/v3/search?key={API_KEY}&part=snippet&type=video&maxResults={MAX_RESULTS}&q={query}"
+    response = requests.get(url)
+    data = json.loads(response.text)
+    video_ids = [item['id']['videoId'] for item in data['items']]
+    video_titles = [item['snippet']['title'] for item in data['items']]
+    return video_ids, video_titles
+
+# Retrieve video transcripts using the YouTube Transcript API
+def get_video_transcripts(video_ids):
+    transcripts = []
+    for video_id in video_ids:
+        try:
+            transcript = YouTubeTranscriptApi.get_transcript(video_id)
+            text = ' '.join([line['text'] for line in transcript])
+            transcripts.append(text)
+        except:
+            transcripts.append('')
+    return transcripts
+
+def get_best_video(input_text: str):
+    start_time = time.time()  # Start measuring time
+
+    # Encode the input text
+    input_embedding = model.encode([input_text], convert_to_tensor=True)
+
+    # Search for videos and retrieve video transcripts
+    video_ids, video_titles = search_videos(input_text)
+    video_transcripts = get_video_transcripts(video_ids)
+
+    # Encode the video transcripts
+    video_embeddings = model.encode(video_transcripts, convert_to_tensor=True)
+
+    # Calculate the similarity between the input text and video transcripts
+    similarity_scores = cosine_similarity(input_embedding, video_embeddings)
+
+    # Rank the videos based on similarity scores
+    ranked_videos = sorted(zip(video_ids, video_titles, similarity_scores), key=lambda x: x[2], reverse=True)
+
+    # Select the top-ranked video ID as the best match
+    best_video_id = ranked_videos[0][0]
+
+    # Construct the YouTube video URL
+    best_video_url = f"https://www.youtube.com/watch?v={best_video_id}"
+
+    end_time = time.time()  # Stop measuring time
+    elapsed_time = end_time - start_time
+
+    return {"best_video_url": best_video_url, "elapsed_time": elapsed_time}
+
+
+# Example usage:
+input_text = "machine learning tutorial"
+best_video = get_best_video(input_text)
+print(best_video)

Backend/__pycache__/Final_LatestSorter.cpython-310.pyc

@@ -12,14 +12,14 @@
 #from Backend.Notes_Analyser import router as api4_router
 #from Backend.Narrator import router as api5_router
 #from Backend.NotesChunker import app as chunker 
-#from Backend.Final_NotesToText import router as notestotxt
-from Backend.Final_Processor import app as processor
-from Backend.Final_NotesChunker import app as chunker
-from Backend.Final_LatestSorter import app as sorter
-from Backend.Final_Sections_topics_json import app as cardmaker
-from Backend.Final_Notes_gen import app as notesgen
-from Backend.Final_Questionare_Creater import app as mcq_gen
-from Backend.Final_email_test import router as email_sender
+from Backend.Final_NotesToText import router as notestotxt
+#from Backend.Final_Processor import app as processor
+#from Backend.Final_NotesChunker import app as chunker
+#from Backend.Final_LatestSorter import app as sorter
+#from Backend.Final_Sections_topics_json import app as cardmaker
+#from Backend.Final_Notes_gen import app as notesgen
+#from Backend.Final_Questionare_Creater import app as mcq_gen
+#from Backend.Final_email_test import router as email_sender
 #from Backend.SortedPQYsender import app as pyqsender
 #from Backend.Perfect_video import app as videofinder
 
@@ -44,14 +44,14 @@
 # Mount the API routerss
 
 
-#app.include_router(notestotxt)
-app.include_router(email_sender)
-app.include_router(processor)
-app.include_router(chunker)
-app.include_router(sorter)
-app.include_router(cardmaker)
-app.include_router(notesgen)
-app.include_router(mcq_gen)
+app.include_router(notestotxt)
+#app.include_router(email_sender)
+#app.include_router(processor)
+#app.include_router(chunker)
+#app.include_router(sorter)
+#app.include_router(cardmaker)
+#app.include_router(notesgen)
+#app.include_router(mcq_gen)
 #app.include_router(chunker)
 #app.include_router(pyqsender)