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  1. .gitattributes +1 -0
  2. README.md +12 -0
  3. app.py +213 -0
  4. cookies.txt +518 -0
  5. requirements.txt +23 -0
  6. video_data/input_vid.mp4 +3 -0
.gitattributes ADDED
@@ -0,0 +1 @@
 
 
1
+ video_data/input_vid.mp4 filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ title: Vega
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+ emoji: 📊
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+ colorFrom: gray
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+ colorTo: yellow
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+ sdk: gradio
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+ sdk_version: 5.43.1
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+ app_file: app.py
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+ pinned: false
10
+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ # -*- coding: utf-8 -*-
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+ # Install required libraries if running outside Colab
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+ # !pip install gradio yt-dlp moviepy pillow speechrecognition llama-index lancedb google-generativeai
4
+
5
+ import gradio as gr
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+ from moviepy import VideoFileClip
7
+ from pathlib import Path
8
+ import speech_recognition as sr
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+ from PIL import Image
10
+ import os
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+ import shutil
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+ import json
13
+ import matplotlib.pyplot as plt
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+
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+ # Add your existing methods here (download_video, video_to_images, video_to_audio, audio_to_text, prepare_video...)
16
+
17
+ def plot_images(image_paths):
18
+ images_shown = 0
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+ plt.figure(figsize=(16, 9))
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+ img_files = []
21
+ for img_path in image_paths:
22
+ if os.path.isfile(img_path):
23
+ img_files.append(img_path)
24
+ images_shown += 1
25
+ if images_shown >= 7:
26
+ break
27
+ return img_files
28
+
29
+ def download_video(video_url, output_video_path="./video_data/"):
30
+ ydl_opts = {
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+ "format": "bestvideo+bestaudio/best",
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+ "merge_output_format": "mp4",
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+ "outtmpl": f"{output_video_path}/input_vid.mp4",
34
+ "noplaylist": True,
35
+ "quiet": False,
36
+ # Uncomment and set your cookie file path if required
37
+ # "cookiefile": "cookies.txt",
38
+ }
39
+ Path(output_video_path).mkdir(parents=True, exist_ok=True)
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+ with yt_dlp.YoutubeDL(ydl_opts) as ydl:
41
+ info = ydl.extract_info(video_url, download=True)
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+ info = ydl.sanitize_info(info)
43
+ return {
44
+ "title": info.get("title"),
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+ "uploader": info.get("uploader"),
46
+ "views": info.get("view_count"),
47
+ }
48
+
49
+ def video_to_images(video_path, output_folder):
50
+ Path(output_folder).mkdir(parents=True, exist_ok=True)
51
+ clip = VideoFileClip(video_path)
52
+ clip.write_images_sequence(
53
+ os.path.join(output_folder, "frame%04d.png"), fps=0.2
54
+ )
55
+
56
+ def video_to_audio(video_path, output_audio_path):
57
+ clip = VideoFileClip(video_path)
58
+ audio = clip.audio
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+ audio.write_audiofile(output_audio_path)
60
+
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+ def audio_to_text(audio_path):
62
+ recognizer = sr.Recognizer()
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+ try:
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+ with sr.AudioFile(audio_path) as source:
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+ audio_data = recognizer.record(source)
66
+ text = recognizer.recognize_google(audio_data)
67
+ return text
68
+ except sr.UnknownValueError:
69
+ print("Google Speech Recognition could not understand the audio.")
70
+ except sr.RequestError as e:
71
+ print(f"Could not request results: {e}")
72
+ return None
73
+
74
+ def prepare_video(video_url,
75
+ output_video_path="./video_data/",
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+ output_folder="./mixed_data/",
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+ output_audio_path="./mixed_data/output_audio.wav"):
78
+ filepath = os.path.join(output_video_path, "input_vid.mp4")
79
+ #meta = download_video(video_url, output_video_path)
80
+ video_to_images(filepath, output_folder)
81
+ video_to_audio(filepath, output_audio_path)
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+ text_data = audio_to_text(output_audio_path)
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+ text_path = os.path.join(output_folder, "output_text.txt")
84
+ with open(text_path, "w") as file:
85
+ file.write(text_data if text_data else "")
86
+ os.remove(output_audio_path)
87
+ meta = {
88
+ "title": "test",
89
+ "uploader": "uploader",
90
+ "views": "view_count",
91
+ }
92
+ return meta, text_data
93
+
94
+ from llama_index.core.indices import MultiModalVectorStoreIndex
95
+ from llama_index.core import SimpleDirectoryReader, StorageContext
96
+ from llama_index.vector_stores.lancedb import LanceDBVectorStore
97
+ from llama_index.embeddings.huggingface import HuggingFaceEmbedding
98
+ from llama_index.core import Settings
99
+
100
+ def create_vector_db(image_txt_folder_path: str):
101
+ text_store = LanceDBVectorStore(uri="lancedb", table_name="text_collection")
102
+ image_store = LanceDBVectorStore(uri="lancedb", table_name="image_collection")
103
+ storage_context = StorageContext.from_defaults(
104
+ vector_store=text_store, image_store=image_store
105
+ )
106
+ Settings.embed_model = HuggingFaceEmbedding(
107
+ model_name="sentence-transformers/all-MiniLM-L6-v2"
108
+ )
109
+ documents = SimpleDirectoryReader(image_txt_folder_path).load_data()
110
+ index = MultiModalVectorStoreIndex.from_documents(
111
+ documents,
112
+ storage_context=storage_context,
113
+ )
114
+ retriever_engine = index.as_retriever(
115
+ similarity_top_k=2, image_similarity_top_k=3
116
+ )
117
+ return retriever_engine
118
+
119
+ from llama_index.core.schema import ImageNode
120
+
121
+ def retrieve(retriever_engine, query_str):
122
+ retrieval_results = retriever_engine.retrieve(query_str)
123
+ retrieved_image = []
124
+ retrieved_text = []
125
+ for res_node in retrieval_results:
126
+ if isinstance(res_node.node, ImageNode):
127
+ retrieved_image.append(res_node.node.metadata["file_path"])
128
+ else:
129
+ retrieved_text.append(res_node.text)
130
+ return retrieved_image, retrieved_text
131
+
132
+ qa_tmpl_str = (
133
+ "Given the provided information, including relevant images and retrieved context from the video, \
134
+ accurately and precisely answer the query without any additional prior knowledge.\n"
135
+ "Please ensure honesty and responsibility, refraining from any racist or sexist remarks.\n"
136
+ "---------------------\n"
137
+ "Context: {context_str}\n"
138
+ "Metadata for video: {metadata_str} \n"
139
+ "---------------------\n"
140
+ "Query: {query_str}\n"
141
+ "Answer: "
142
+ )
143
+
144
+ import google.generativeai as genai
145
+
146
+ def get_response(retriever_engine, query_str, metadata_str, output_folder):
147
+ img, txt = retrieve(retriever_engine=retriever_engine, query_str=query_str)
148
+ context_str = "".join(txt)
149
+ prompt = qa_tmpl_str.format(
150
+ context_str=context_str, query_str=query_str, metadata_str=metadata_str
151
+ )
152
+
153
+ GOOGLE_API_KEY = "AIzaSyD0sn-z1CmYcyhzSyE_4t2_nSQFGmnKFWc"
154
+ genai.configure(api_key=GOOGLE_API_KEY)
155
+ gemini_model = genai.GenerativeModel('gemini-1.5-flash-latest')
156
+
157
+
158
+ content_parts = [prompt]
159
+ image_paths = []
160
+ for img_path in img:
161
+ try:
162
+ image = Image.open(img_path)
163
+ content_parts.append(image)
164
+ image_paths.append(img_path)
165
+ except Exception as e:
166
+ print(f"Error loading image {img_path}: {e}")
167
+
168
+ response_1 = gemini_model.generate_content(content_parts)
169
+ result_text = response_1.text if hasattr(response_1, 'text') else str(response_1)
170
+ return result_text, image_paths
171
+
172
+ # Gradio interface function
173
+ def gradio_chat(query):
174
+ output_video_path = "./video_data/"
175
+ output_folder = "./mixed_data/"
176
+ output_audio_path = "./mixed_data/output_audio.wav"
177
+ video_url=""
178
+
179
+ try:
180
+ metadata_vid, text_data = prepare_video(
181
+ video_url, output_video_path, output_folder, output_audio_path
182
+ )
183
+ metadata_str = json.dumps(metadata_vid)
184
+ retriever_engine = create_vector_db(output_folder)
185
+ result_text, image_paths = get_response(
186
+ retriever_engine, query, metadata_str, output_folder
187
+ )
188
+ # Cleanup
189
+ #if os.path.exists(output_video_path):
190
+ # shutil.rmtree(output_video_path)
191
+ #if os.path.exists(output_folder):
192
+ # shutil.rmtree(output_folder)
193
+ # Gradio can return text plus images (as list of file paths)
194
+ return result_text, image_paths
195
+ except Exception as e:
196
+ return f"Error: {str(e)}", []
197
+
198
+ # Gradio UI
199
+ gradio_ui = gr.Interface(
200
+ fn=gradio_chat,
201
+ inputs=[
202
+ gr.Textbox(label="Try:Beaches in Kerala"),
203
+ ],
204
+ outputs=[
205
+ gr.Textbox(label="Vega Response"),
206
+ gr.Gallery(label="Relevant Images", allow_preview=True),
207
+ ],
208
+ title="",
209
+ description=""
210
+ )
211
+
212
+ if __name__ == "__main__":
213
+ gradio_ui.launch(share=True)
cookies.txt ADDED
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