Create app.py

app.py

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+import gradio as gr
+import os
+import whisper
+import cv2
+import json
+import tempfile
+import torch
+import transformers
+import re
+import time
+from torch import cuda, bfloat16
+from moviepy.editor import VideoFileClip
+from image_caption import Caption
+from pathlib import Path
+from langchain import PromptTemplate
+from langchain import LLMChain
+from langchain.llms import HuggingFacePipeline
+from difflib import SequenceMatcher
+import argparse
+import shutil
+import google.generativeai as genai
+
+class VideoClassifier:
+    def __init__(self, no_of_frames, mode='interface'):
+        self.no_of_frames = no_of_frames
+        self.mode = mode
+        os.environ["TOKENIZERS_PARALLELISM"] = "false"
+        # self.setup_model()
+        self.setup_paths()
+        self.setup_gemini_model()
+
+    def setup_paths(self):
+        self.path = './results'
+        if os.path.exists(self.path):
+            shutil.rmtree(self.path)  # Remove the directory if it exists
+        os.mkdir(self.path)
+
+    def setup_gemini_model(self):
+        self.genai = genai
+        self.genai.configure(api_key="AIzaSyAFG94rVbm9eWepO5uPGsMha8XJ-sHbMdA")
+        self.genai_model = genai.GenerativeModel('gemini-pro')
+        self.whisper_model = whisper.load_model("base")
+        self.img_cap = Caption()
+
+    def setup_model(self):
+        self.model_id = "mistralai/Mistral-7B-Instruct-v0.2"
+        self.device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
+        self.device_name = torch.cuda.get_device_name()
+        # print(f"Using device: {self.device} ({self.device_name})")
+        bnb_config = transformers.BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_quant_type='nf4',
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_compute_dtype=bfloat16
+        )
+        hf_auth = hf_key
+        model_config = transformers.AutoConfig.from_pretrained(
+            self.model_id,
+            use_auth_token=hf_auth
+        )
+        self.model = transformers.AutoModelForCausalLM.from_pretrained(
+            self.model_id,
+            trust_remote_code=True,
+            config=model_config,
+            quantization_config=bnb_config,
+            device_map='auto',
+            use_auth_token=hf_auth
+        )
+        self.model.eval()
+        self.tokenizer = transformers.AutoTokenizer.from_pretrained(
+            self.model_id,
+            use_auth_token=hf_auth
+        )
+        self.generate_text = transformers.pipeline(
+            model=self.model, tokenizer=self.tokenizer,
+            return_full_text=True,
+            task='text-generation',
+            temperature=0.01,
+            max_new_tokens=32
+        )
+        self.whisper_model = whisper.load_model("base")
+        self.img_cap = Caption()
+        self.llm = HuggingFacePipeline(pipeline=self.generate_text)
+
+    def classify_video(self, video_input):
+        print(f"Processing video: {video_input} with {self.no_of_frames} frames.")
+        start = time.time()
+        mp4_file = video_input
+        video_name = mp4_file.split("/")[-1]
+        wav_file = "results/audiotrack.wav"
+        video_clip = VideoFileClip(mp4_file)
+        audioclip = video_clip.audio
+        wav_file = audioclip.write_audiofile(wav_file)
+        audioclip.close()
+        video_clip.close()
+        audiotrack = "results/audiotrack.wav"
+        result = self.whisper_model.transcribe(audiotrack, fp16=False)
+        transcript = result["text"]
+        print("TRANSCRIPT",transcript)
+        # print("####transcript length:", len(transcript))
+        end = time.time()
+        time_taken_1 = round(end - start, 3)
+        # print("Time taken from video to transcript:", time_taken_1)
+
+        path = './results/frames'
+        if not os.path.exists(path):
+            os.mkdir(path)
+        video = cv2.VideoCapture(video_input)
+        length = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+        # print(f'There are {length} frames in this video')
+        # args = self.parser.parse_args()
+        no_of_frame = int(self.no_of_frames)
+        temp_div = length//(no_of_frame)
+        currentframe = 50
+        caption_text = []
+        for i in range(no_of_frame):
+            # reading from frame
+            video.set(cv2.CAP_PROP_POS_FRAMES, currentframe)
+            ret,frame = video.read()
+            if ret:
+                # if video is still left continue creating images
+                name = './results/frames/frame' + str(currentframe) + '.jpg'
+                # writing the extracted images
+                # print ('Creating...' + name)
+                cv2.imwrite(name, frame)
+                image_paths = []
+                img_path = 'results/frames/frame' + str(currentframe) + '.jpg'
+                image_paths.append(img_path)
+                content = self.img_cap.predict_step(image_paths)
+                print("content",content)
+                caption_text.append(content[0])
+                currentframe += temp_div-1
+            else:
+                break
+        def listToString(s):
+            str1 = ", "
+            return (str1.join(s))
+        captions = listToString(caption_text)
+        print("CAPTIONS",captions)
+
+        end1 = time.time()
+        time_taken_2 = round(end1 - end, 3)
+        # print("Time taken from transcript to image_caption:", time_taken_2)
+        video.release()
+        cv2.destroyAllWindows()
+
+        main_categories = Path("categories/main_classes.txt").read_text()
+        main_categories_list = ['Automotive', 'Books and Literature', 'Business and Finance', 'Careers', 'Education','Family and Relationships',
+        'Fine Art', 'Food & Drink', 'Healthy Living', 'Hobbies & Interests', 'Home & Garden','Medical Health', 'Movies', 'Music and Audio', 
+        'News and Politics', 'Personal Finance', 'Pets', 'Pop Culture','Real Estate', 'Religion & Spirituality', 'Science', 'Shopping', 'Sports', 
+        'Style & Fashion','Technology & Computing', 'Television', 'Travel', 'Video Gaming']
+
+
+        template1 = '''Given below are the different type of main video classes  
+
+        {main_categories}
+
+        You are a text classifier that catergorises the transcript and captions into one main class whose context match with one main class and only generate main class name no need of sub classe or explanation.
+        Give more importance to Transcript while classifying .
+        Transcript: {transcript}
+
+        Captions: {captions}
+
+        Return only the answer chosen from list and nothing else
+        Main-class =>  '''
+
+        prompt1 = PromptTemplate(template=template1, input_variables=['main_categories', 'transcript', 'captions'])
+        print("PROMPT 1",prompt1)
+        prompt_text = template1.format(main_categories=main_categories, transcript=transcript, captions=captions)
+        start2 = time.time()
+        time_taken_3 = round(start2 - end1, 3)
+        response = self.genai_model.generate_content(contents=prompt_text)
+        main_class = response.text
+        
+        print(main_class)
+        print("#######################################################")
+        # pattern = r"Main-class =>\s*(.+)"
+        # match = re.search(pattern, main_class)
+        # if match:
+        #     main_class = match.group(1).strip()
+        # else:
+        #     main_class = None
+        # print("MAIN CLASS: ",main_class)
+        def category_class(class_name,categories_list):
+            def similar(str1, str2):
+                return SequenceMatcher(None, str1, str2).ratio()
+            index_no = 0
+            sim = 0
+            for sub in categories_list:
+                res = similar(class_name, sub)
+                if res>sim:
+                    sim = res
+                    index_no = categories_list.index(sub)
+            class_name = categories_list[index_no]
+            return class_name
+        
+        if main_class not in main_categories_list:
+            main_class = category_class(main_class,main_categories_list)
+        print("POST PROCESSED MAIN CLASS : ",main_class)
+        tier_1_index_no = main_categories_list.index(main_class) + 1
+
+        with open('categories/categories_json.txt') as f:
+            data = json.load(f)
+        sub_categories_list = data[main_class]
+        print("SUB CATEGORIES LIST",sub_categories_list)
+        with open("categories/sub_categories.txt", "w") as f:
+            no = 1
+            
+            # print(data[main_class])
+            for i in data[main_class]:
+                f.write(str(no)+')'+str(i) + '\n')
+                no = no+1
+        sub_categories = Path("categories/sub_categories.txt").read_text()
+
+        template2 = '''Given below are the sub classes of {main_class}.
+
+        {sub_categories}
+
+        You are a text classifier that catergorises the transcript and captions into one sub class whose context match with one sub class and only generate sub class name, Don't give explanation .
+        Give more importance to Transcript while classifying .
+        Transcript: {transcript}
+
+        Captions: {captions}
+
+        Return only the Sub-class answer chosen from list and nothing else
+        Answer in the format:
+        Main-class => {main_class}
+        Sub-class => 
+         '''
+        
+        prompt2 = PromptTemplate(template=template2, input_variables=['sub_categories', 'transcript', 'captions','main_class'])
+        prompt_text2 = template1.format(main_categories=main_categories, transcript=transcript, captions=captions)
+        start2 = time.time()
+        time_taken_3 = round(start2 - end1, 3)
+        response = self.genai_model.generate_content(contents=prompt_text2)
+        sub_class = response.text
+        print("Preprocess Answer",sub_class)
+        end2 = time.time()
+        time_taken_predict = round(end2 - start2, 3)
+        time_taken_total = round(end2 - start, 3)
+        # print("Time taken by model to predict:", time_taken_predict)
+        # print("Total time taken:", time_taken_total)
+        
+
+        # pattern = r"Sub-class =>\s*(.+)"
+        # match = re.search(pattern, sub_class)
+        # if match:
+        #     sub_class = match.group(1).strip()
+        # else:
+        #     sub_class = None
+        # print("SUB CLASS",sub_class)
+        if sub_class not in sub_categories_list:
+            sub_class = category_class(sub_class,sub_categories_list)
+            print("POST PROCESSED SUB CLASS",sub_class)
+        tier_2_index_no = sub_categories_list.index(sub_class) + 1
+        print("ANSWER:",sub_class)
+        final_answer = (f"Tier 1 category : IAB{tier_1_index_no} : {main_class}\nTier 2 category : IAB{tier_1_index_no}-{tier_2_index_no} : {sub_class}")
+
+        first_video = os.path.join(os.path.dirname(__file__), "videos/American_football_heads_to_India_clip.mp4")
+        second_video = os.path.join(os.path.dirname(__file__), "videos/PersonalFinance_clip.mp4")
+
+        # return final_answer, first_video, second_video
+        return final_answer
+
+    def launch_interface(self):
+        css_code = """
+        .gradio-container {background-color: #000000;color:#FFFFFF;background-size: 200px; background-image:url(https://gitlab.ignitarium.in/saran/logo/-/raw/aab7c77b4816b8a4bbdc5588eb57ce8b6c15c72d/ign_logo_white.png);background-repeat:no-repeat; position:absolute; top:1px; left:5px;}
+        .gradio-container-4-1-2 .prose h1 {color:#FFFFFF !important }
+        .body {background-color: #000000 !important}
+        @media screen and (max-width: 1200px) {
+        .gradio-container-4-1-2 .prose h1 {color:#FFFFFF !important; margin-top: 6%}
+        }
+        .built-with svelte-mpyp5e {visibility:hidden}
+        .show-api svelte-mpyp5e {visibility:hidden}
+        """
+        css_code += """
+        :root {
+        --body-background-fill: #000000; /* New value */
+        }
+        """
+        demo = gr.Interface(fn=self.classify_video, inputs="playablevideo",allow_flagging='never', examples=[
+                            os.path.join(os.path.dirname(__file__), 
+                                         "videos/American_football_heads_to_India_clip.mp4"),os.path.join(os.path.dirname(__file__), "videos/PersonalFinance_clip.mp4"),
+                                         os.path.join(os.path.dirname(__file__), "videos/Motorcycle_clip.mp4"),
+                                         os.path.join(os.path.dirname(__file__), "videos/Spirituality_1_clip.mp4"),
+                                         os.path.join(os.path.dirname(__file__), "videos/Science_clip.mp4")], 
+                        cache_examples=False, 
+                        # outputs=["text", gr.Video(height=80, width=120), gr.Video(height=80, width=120)],
+                        outputs=["text"],
+                        css=css_code, title="Interactive Advertising Bureau (IAB) compliant Video-Ad classification")
+        demo.launch(debug=True)
+    
+    def run_inference(self, video_path):
+        result = self.classify_video(video_path)
+        print(result)
+    
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Process some videos.')
+    parser.add_argument("video_path", nargs='?', default=None, help="Path to the video file")
+    parser.add_argument("-n", "--no_of_frames", type=int, default=8, help="Number of frames for image captioning")
+    parser.add_argument("--mode", choices=['interface', 'inference'], default='interface', help="Mode of operation: interface or inference")
+    
+    args = parser.parse_args()
+
+    vc = VideoClassifier(no_of_frames=args.no_of_frames, mode=args.mode)
+    
+    if args.mode == 'interface':
+        vc.launch_interface()
+    elif args.mode == 'inference' and args.video_path:
+        vc.run_inference(args.video_path)
+    else:
+        print("Error: No video path provided for inference mode.")
+
+### python main.py --mode interface
+### python main.py videos/Spirituality_1_clip.mp4 -n 3 --mode inference