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app.py

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+import gradio as gr
+import torch
+import os
+import subprocess
+import json
+from threading import Thread
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import spaces
+import moviepy.editor as mp
+import time
+import langdetect
+import uuid
+from dotenv import load_dotenv
+
+load_dotenv()
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+print("Starting the program...")
+
+model_path = "internlm/internlm2_5-7b-chat"
+print(f"Loading model {model_path}...")
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
+model = model.eval()
+print("Model successfully loaded.")
+
+def generate_unique_filename(extension):
+    return f"{uuid.uuid4()}{extension}"
+
+def cleanup_files(*files):
+    for file in files:
+        if file and os.path.exists(file):
+            os.remove(file)
+            print(f"Removed file: {file}")
+
+@spaces.GPU(duration=90)
+def transcribe_audio(file_path):
+    print(f"Starting transcription of file: {file_path}")
+    temp_audio = None
+    if file_path.endswith(('.mp4', '.avi', '.mov', '.flv')):
+        print("Video file detected. Extracting audio...")
+        try:
+            video = mp.VideoFileClip(file_path)
+            temp_audio = generate_unique_filename(".wav")
+            video.audio.write_audiofile(temp_audio)
+            file_path = temp_audio
+        except Exception as e:
+            print(f"Error extracting audio from video: {e}")
+            raise
+    
+    print(f"Does the file exist? {os.path.exists(file_path)}")
+    print(f"File size: {os.path.getsize(file_path) if os.path.exists(file_path) else 'N/A'} bytes")
+    
+    output_file = generate_unique_filename(".json")
+    command = [
+        "insanely-fast-whisper",
+        "--file-name", file_path,
+        "--device-id", "0",
+        "--model-name", "openai/whisper-large-v3",
+        "--task", "transcribe",
+        "--timestamp", "chunk",
+        "--transcript-path", output_file
+    ]
+    print(f"Executing command: {' '.join(command)}")
+    try:
+        result = subprocess.run(command, check=True, capture_output=True, text=True)
+        print(f"Standard output: {result.stdout}")
+        print(f"Error output: {result.stderr}")
+    except subprocess.CalledProcessError as e:
+        print(f"Error running insanely-fast-whisper: {e}")
+        print(f"Standard output: {e.stdout}")
+        print(f"Error output: {e.stderr}")
+        raise
+    
+    print(f"Reading transcription file: {output_file}")
+    try:
+        with open(output_file, "r") as f:
+            transcription = json.load(f)
+    except json.JSONDecodeError as e:
+        print(f"Error decoding JSON: {e}")
+        print(f"File content: {open(output_file, 'r').read()}")
+        raise
+    
+    if "text" in transcription:
+        result = transcription["text"]
+    else:
+        result = " ".join([chunk["text"] for chunk in transcription.get("chunks", [])])
+    
+    print("Transcription completed.")
+    
+    # Cleanup
+    cleanup_files(output_file)
+    if temp_audio:
+        cleanup_files(temp_audio)
+    
+    return result
+
+@spaces.GPU(duration=90)
+def generate_summary_stream(transcription):
+    print("Starting summary generation...")
+    print(f"Transcription length: {len(transcription)} characters")
+    
+    detected_language = langdetect.detect(transcription)
+    
+    prompt = f"""Summarize the following video transcription in 200-300 words. 
+    The summary should be in the same language as the transcription, which is detected as {detected_language}.
+    Please ensure that the summary captures the main points and key ideas of the transcription:
+    {transcription[:300000]}..."""
+    
+    response, history = model.chat(tokenizer, prompt, history=[])
+    print(f"Final summary generated: {response[:100]}...")
+    print("Summary generation completed.")
+    return response
+
+def process_uploaded_video(video_path):
+    print(f"Processing uploaded video: {video_path}")
+    try:
+        print("Starting transcription...")
+        transcription = transcribe_audio(video_path)
+        print(f"Transcription completed. Length: {len(transcription)} characters")
+        return transcription, None
+    except Exception as e:
+        print(f"Error processing video: {e}")
+        return f"Processing error: {str(e)}", None
+
+print("Setting up Gradio interface...")
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🎥 Video Transcription and Smart Summary
+        
+        Upload a video to get a transcription and AI-generated summary.
+        """
+    )
+    
+    with gr.Tabs():
+        with gr.TabItem("📤 Video Upload"):
+            video_input = gr.Video(label="Drag and drop or click to upload")
+            video_button = gr.Button("🚀 Process Video", variant="primary")       
+       
+    
+    with gr.Row():
+        with gr.Column():
+            transcription_output = gr.Textbox(label="📝 Transcription", lines=10, show_copy_button=True)
+        with gr.Column():
+            summary_output = gr.Textbox(label="📊 Summary", lines=10, show_copy_button=True)
+    
+    summary_button = gr.Button("📝 Generate Summary", variant="secondary")
+    
+    gr.Markdown(
+        """
+        ### How to use:
+        1. Upload a video.
+        2. Click 'Process' to get the transcription.
+        3. Click 'Generate Summary' to get a summary of the content.
+        
+        *Note: Processing may take a few minutes depending on the video length.*
+        """
+    )
+    
+    def process_video_and_update(video):
+        if video is None:
+            return "No video uploaded.", "Please upload a video."
+        print(f"Video received: {video}")
+        transcription, _ = process_uploaded_video(video)
+        print(f"Returned transcription: {transcription[:100] if transcription else 'No transcription generated'}...")
+        return transcription or "Transcription error", ""
+
+    video_button.click(process_video_and_update, inputs=[video_input], outputs=[transcription_output, summary_output])   
+    summary_button.click(generate_summary_stream, inputs=[transcription_output], outputs=[summary_output])
+
+print("Launching Gradio interface...")
+demo.launch()

requirements.txt

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+insanely-fast-whisper
+accelerate
+bitsandbytes
+scipy
+sentencepiece
+spaces
+tiktoken
+pytest
+torch
+transformers
+moviepy
+langdetect
+python-dotenv