Update app.py

app.py

@@ -1,416 +1,399 @@
-import streamlit as st
-import tempfile
-import os
-import torch
-from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq, AutoTokenizer, AutoModelForSeq2SeqLM
-import librosa
-import numpy as np
-import ffmpeg
-import time
-import json
-import psutil
-
-def format_time(seconds):
-    minutes = int(seconds // 60)
-    secs = int(seconds % 60)
-    return f"{minutes}:{secs:02d}"
-
-def seconds_to_srt_time(seconds):
-    hours = int(seconds // 3600)
-    minutes = int((seconds % 3600) // 60)
-    secs = int(seconds % 60)
-    millis = int((seconds - int(seconds)) * 1000)
-    return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}"
-
-@st.cache_resource
-def load_model(language='en', summarizer_type='bart'):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    if language == 'ur':
-        processor = AutoProcessor.from_pretrained("GogetaBlueMUI/whisper-medium-ur-fleurs")
-        model = AutoModelForSpeechSeq2Seq.from_pretrained("GogetaBlueMUI/whisper-medium-ur-fleurs").to(device)
-    else:
-        processor = AutoProcessor.from_pretrained("openai/whisper-small")
-        model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small").to(device)
-    if device.type == "cuda":
-        model = model.half()
-    if summarizer_type == 'bart':
-        sum_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
-        sum_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn").to(device)
-    else:
-        sum_tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-large-book-summary")
-        sum_model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-large-book-summary").to(device)
-    return processor, model, sum_tokenizer, sum_model, device
-
-def split_audio_into_chunks(audio, sr, chunk_duration):
-    chunk_samples = int(chunk_duration * sr)
-    chunks = [audio[start:start + chunk_samples] for start in range(0, len(audio), chunk_samples)]
-    return chunks
-
-def transcribe_audio(audio, sr, processor, model, device, start_time, language, task="transcribe"):
-    inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
-    input_features = inputs.input_features.to(device)
-    if model.dtype == torch.float16:
-        input_features = input_features.half()
-    generate_kwargs = {
-        "task": task,
-        "language": "urdu" if language == "ur" else language,
-        "max_new_tokens": 128,
-        "return_timestamps": True
-    }
-    try:
-        with torch.no_grad():
-            outputs = model.generate(input_features, **generate_kwargs)
-        text = processor.decode(outputs[0], skip_special_tokens=True)
-        return [(text, start_time, start_time + len(audio) / sr)]
-    except Exception as e:
-        st.error(f"Transcription error: {str(e)}")
-        return [(f"Error: {str(e)}", start_time, start_time + len(audio) / sr)]
-
-def process_chunks(chunks, sr, processor, model, device, language, chunk_duration, task="transcribe", transcript_file="temp_transcript.json"):
-    transcript = []
-    chunk_start = 0
-    total_chunks = len(chunks)
-    progress_bar = st.progress(0)
-    status_text = st.empty()
-    if os.path.exists(transcript_file):
-        os.remove(transcript_file)
-    for i, chunk in enumerate(chunks):
-        status_text.text(f"Processing chunk {i+1}/{total_chunks}...")
-        try:
-            memory = psutil.virtual_memory()
-            st.write(f"Memory usage: {memory.percent}% (Chunk {i+1}/{total_chunks})")
-            chunk_transcript = transcribe_audio(chunk, sr, processor, model, device, chunk_start, language, task)
-            transcript.extend(chunk_transcript)
-            with open(transcript_file, "w", encoding="utf-8") as f:
-                json.dump(transcript, f, ensure_ascii=False)
-            chunk_start += chunk_duration
-            progress_bar.progress((i + 1) / total_chunks)
-        except Exception as e:
-            st.error(f"Error processing chunk {i+1}: {str(e)}")
-            break
-    status_text.text("Processing complete!")
-    progress_bar.empty()
-    return transcript
-
-def summarize_text(text, tokenizer, model, device, summarizer_type='bart'):
-    if summarizer_type == 'bart':
-        max_input_length = 1024
-        max_summary_length = 150
-        chunk_size = 512
-    else:
-        max_input_length = 16384
-        max_summary_length = 512
-        chunk_size = 8192
-    inputs = tokenizer(text, return_tensors="pt", truncation=False)
-    input_ids = inputs["input_ids"].to(device)
-    num_tokens = input_ids.shape[1]
-    st.write(f"Number of tokens in input: {num_tokens}")
-    if num_tokens < 50:
-        return "Transcript too short to summarize effectively."
-    try:
-        summaries = []
-        if num_tokens <= max_input_length:
-            truncated_inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=max_input_length).to(device)
-            with torch.no_grad():
-                summary_ids = model.generate(truncated_inputs["input_ids"], num_beams=4, max_length=max_summary_length, min_length=50, early_stopping=True, temperature=0.7)
-            summaries.append(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
-        else:
-            st.write(f"Transcript exceeds {max_input_length} tokens. Processing in chunks...")
-            tokens = input_ids[0].tolist()
-            for i in range(0, num_tokens, chunk_size):
-                chunk_tokens = tokens[i:i + chunk_size]
-                chunk_input_ids = torch.tensor([chunk_tokens]).to(device)
-                with torch.no_grad():
-                    summary_ids = model.generate(chunk_input_ids, num_beams=4, max_length=max_summary_length // 2, min_length=25, early_stopping=True, temperature=0.7)
-                summaries.append(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
-            combined_summary = " ".join(summaries)
-            combined_inputs = tokenizer(combined_summary, return_tensors="pt", truncation=True, max_length=max_input_length).to(device)
-            with torch.no_grad():
-                final_summary_ids = model.generate(combined_inputs["input_ids"], num_beams=4, max_length=max_summary_length, min_length=50, early_stopping=True, temperature=0.7)
-            summaries = [tokenizer.decode(final_summary_ids[0], skip_special_tokens=True)]
-        return " ".join(summaries)
-    except Exception as e:
-        st.error(f"Summarization error: {str(e)}")
-        return f"Error: {str(e)}"
-
-def save_uploaded_file(uploaded_file):
-    try:
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_file:
-            tmp_file.write(uploaded_file.read())
-            return tmp_file.name
-    except Exception as e:
-        st.error(f"Error saving uploaded file: {str(e)}")
-        return None
-
-def merge_intervals(intervals):
-    if not intervals:
-        return []
-    intervals.sort(key=lambda x: x[0])
-    merged = [intervals[0]]
-    for current in intervals[1:]:
-        previous = merged[-1]
-        if previous[1] >= current[0]:
-            merged[-1] = (previous[0], max(previous[1], current[1]))
-        else:
-            merged.append(current)
-    return merged
-
-def create_edited_video(video_path, transcript, keep_indices):
-    try:
-        intervals_to_keep = [(transcript[i][1], transcript[i][2]) for i in keep_indices]
-        merged_intervals = merge_intervals(intervals_to_keep)
-        temp_files = []
-        resolution = st.session_state.get('resolution', '1280:720')  # Default to 720p
-        crf = st.session_state.get('crf', 23)  # Default to medium quality
-        scale_filter = f"scale={resolution}" if resolution else None
-        for j, (start, end) in enumerate(merged_intervals):
-            temp_file = f"temp_{j}.mp4"
-            output_args = {
-                'vcodec': 'libx264',
-                'preset': 'medium',
-                'crf': crf,
-                'r': 30,  # 30 FPS
-                'acodec': 'aac',
-                'ab': '128k',
-                'map_metadata': -1
-            }
-            if scale_filter:
-                output_args['vf'] = scale_filter
-            ffmpeg.input(video_path, ss=start, to=end).output(
-                temp_file, **output_args
-            ).run(overwrite_output=True, quiet=True)
-            temp_files.append(temp_file)
-        with open("list.txt", "w") as f:
-            for temp_file in temp_files:
-                f.write(f"file '{temp_file}'\n")
-        edited_video_path = "edited_video.mp4"
-        ffmpeg.input('list.txt', format='concat', safe=0).output(
-            edited_video_path, **output_args
-        ).run(overwrite_output=True, quiet=True)
-        for temp_file in temp_files:
-            if os.path.exists(temp_file):
-                os.remove(temp_file)
-        if os.path.exists("list.txt"):
-            os.remove("list.txt")
-        return edited_video_path
-    except Exception as e:
-        st.error(f"Error creating edited video: {str(e)}")
-        return None
-
-def generate_srt(transcript, include_timeframe=True):
-    srt_content = ""
-    for idx, (text, start, end) in enumerate(transcript, 1):
-        if include_timeframe:
-            start_time = seconds_to_srt_time(start)
-            end_time = seconds_to_srt_time(end)
-            srt_content += f"{idx}\n{start_time} --> {end_time}\n{text}\n\n"
-        else:
-            srt_content += f"{text}\n\n"
-    return srt_content
-
-def main():
-    st.title("Video Transcription and Summarization App")
-    st.markdown("Upload a video to transcribe its audio, generate a summary, and edit subtitles.")
-
-    if 'app_state' not in st.session_state:
-        st.session_state['app_state'] = 'upload'
-    if 'video_path' not in st.session_state:
-        st.session_state['video_path'] = None
-    if 'primary_transcript' not in st.session_state:
-        st.session_state['primary_transcript'] = None
-    if 'english_transcript' not in st.session_state:
-        st.session_state['english_transcript'] = None
-    if 'english_summary' not in st.session_state:
-        st.session_state['english_summary'] = None
-    if 'language' not in st.session_state:
-        st.session_state['language'] = None
-    if 'language_code' not in st.session_state:
-        st.session_state['language_code'] = None
-    if 'translate_to_english' not in st.session_state:
-        st.session_state['translate_to_english'] = False
-    if 'summarizer_type' not in st.session_state:
-        st.session_state['summarizer_type'] = None
-    if 'summary_generated' not in st.session_state:
-        st.session_state['summary_generated'] = False
-    if 'current_time' not in st.session_state:
-        st.session_state['current_time'] = 0
-    if 'edited_video_path' not in st.session_state:
-        st.session_state['edited_video_path'] = None
-    if 'search_query' not in st.session_state:
-        st.session_state['search_query'] = ""
-    if 'show_timeframe' not in st.session_state:
-        st.session_state['show_timeframe'] = True
-    if 'resolution' not in st.session_state:
-        st.session_state['resolution'] = '1280:720'  # Default to 720p
-    if 'crf' not in st.session_state:
-        st.session_state['crf'] = 23  # Default to medium quality
-
-    st.write(f"Current app state: {st.session_state['app_state']}")
-
-    if st.session_state['app_state'] == 'upload':
-        with st.form(key="upload_form"):
-            uploaded_file = st.file_uploader("Upload a video", type=["mp4"])
-            if st.form_submit_button("Upload") and uploaded_file:
-                video_path = save_uploaded_file(uploaded_file)
-                if video_path:
-                    st.session_state['video_path'] = video_path
-                    st.session_state['app_state'] = 'processing'
-                    st.write(f"Uploaded file: {uploaded_file.name}")
-                    st.rerun()
-
-    if st.session_state['app_state'] == 'processing':
-        with st.form(key="processing_form"):
-            language = st.selectbox("Select language", ["English", "Urdu"], key="language_select")
-            language_code = "en" if language == "English" else "ur"
-            st.session_state['language'] = language
-            st.session_state['language_code'] = language_code
-            chunk_duration = st.number_input("Duration per chunk (seconds):", min_value=1.0, step=0.1, value=10.0)
-            resolution = st.selectbox("Output resolution", ["Original", "1080p", "720p"], key="resolution_select")
-            quality = st.selectbox("Video quality", ["High", "Medium", "Low"], key="quality_select")
-            resolution_map = {"Original": None, "1080p": "1920:1080", "720p": "1280:720"}
-            crf_map = {"High": 18, "Medium": 23, "Low": 28}
-            st.session_state['resolution'] = resolution_map[resolution]
-            st.session_state['crf'] = crf_map[quality]
-            if language_code == "ur":
-                translate_to_english = st.checkbox("Generate English translation", key="translate_checkbox")
-                st.session_state['translate_to_english'] = translate_to_english
-            else:
-                st.session_state['translate_to_english'] = False
-            if st.form_submit_button("Process"):
-                with st.spinner("Processing video..."):
-                    start_time = time.time()
-                    try:
-                        st.write("Extracting audio...")
-                        audio_path = "processed_audio.wav"
-                        ffmpeg.input(st.session_state['video_path']).output(audio_path, ac=1, ar=16000).run(overwrite_output=True, quiet=True)
-                        audio, sr = librosa.load(audio_path, sr=16000)
-                        audio = np.nan_to_num(audio, nan=0.0, posinf=0.0, neginf=0.0)
-                        audio_duration = len(audio) / sr
-                        st.write(f"Audio duration: {audio_duration:.2f} seconds")
-                        if audio_duration < 5:
-                            st.error("Audio too short (< 5s). Upload a longer video.")
-                            return
-                        summarizer_type = 'bart' if audio_duration <= 300 else 'led'
-                        st.write(f"Using summarizer: {summarizer_type}")
-                        st.session_state['summarizer_type'] = summarizer_type
-                        st.write("Loading models...")
-                        processor, model, sum_tokenizer, sum_model, device = load_model(language_code, summarizer_type)
-                        st.write("Splitting audio into chunks...")
-                        chunks = split_audio_into_chunks(audio, sr, chunk_duration)
-                        st.write(f"Number of chunks: {len(chunks)}")
-                        st.write("Transcribing audio...")
-                        primary_transcript = process_chunks(chunks, sr, processor, model, device, language_code, chunk_duration, task="transcribe", transcript_file="temp_primary_transcript.json")
-                        english_transcript = None
-                        if st.session_state['translate_to_english'] and language_code == "ur":
-                            st.write("Translating to English...")
-                            processor, model, _, _, device = load_model('en', summarizer_type)
-                            english_transcript = process_chunks(chunks, sr, processor, model, device, 'ur', chunk_duration, task="translate", transcript_file="temp_english_transcript.json")
-                        st.session_state.update({
-                            'primary_transcript': primary_transcript,
-                            'english_transcript': english_transcript,
-                            'summary_generated': False,
-                            'app_state': 'results'
-                        })
-                        st.write("Processing completed successfully!")
-                        st.rerun()
-                    except Exception as e:
-                        st.error(f"Processing failed: {str(e)}")
-                    finally:
-                        if os.path.exists(audio_path):
-                            os.remove(audio_path)
-                        for temp_file in ["temp_primary_transcript.json", "temp_english_transcript.json"]:
-                            if os.path.exists(temp_file):
-                                os.remove(temp_file)
-
-    if st.session_state['app_state'] == 'results':
-        st.video(st.session_state['video_path'], start_time=st.session_state['current_time'])
-        st.session_state['show_timeframe'] = st.checkbox("Show timeframe in transcript", value=st.session_state['show_timeframe'])
-        st.markdown("### Search Subtitles")
-        search_query = st.text_input("Search subtitles...", value=st.session_state['search_query'] or "", key="search_input")
-        st.session_state['search_query'] = search_query.lower()
-        st.markdown(f"### {st.session_state['language']} Transcript")
-        for text, start, end in st.session_state['primary_transcript']:
-            display_text = text.lower()
-            if not search_query or search_query in display_text:
-                label = f"[{format_time(start)} - {format_time(end)}] {text}" if st.session_state['show_timeframe'] else text
-                if st.button(label, key=f"primary_{start}"):
-                    st.session_state['current_time'] = start
-                    st.rerun()
-        if st.session_state['english_transcript']:
-            st.markdown("### English Translation")
-            for text, start, end in st.session_state['english_transcript']:
-                display_text = text.lower()
-                if not search_query or search_query in display_text:
-                    label = f"[{format_time(start)} - {format_time(end)}] {text}" if st.session_state['show_timeframe'] else text
-                    if st.button(label, key=f"english_{start}"):
-                        st.session_state['current_time'] = start
-                        st.rerun()
-        if (st.session_state['language_code'] == 'en' or st.session_state['translate_to_english']) and not st.session_state['summary_generated']:
-            if st.button("Generate Summary"):
-                with st.spinner("Generating summary..."):
-                    try:
-                        _, _, sum_tokenizer, sum_model, device = load_model(st.session_state['language_code'], st.session_state['summarizer_type'])
-                        full_text = " ".join([text for text, _, _ in (st.session_state['english_transcript'] or st.session_state['primary_transcript'])])
-                        english_summary = summarize_text(full_text, sum_tokenizer, sum_model, device, st.session_state['summarizer_type'])
-                        st.session_state['english_summary'] = english_summary
-                        st.session_state['summary_generated'] = True
-                    except Exception as e:
-                        st.error(f"Summary generation failed: {str(e)}")
-        if st.session_state['english_summary'] and st.session_state['summary_generated']:
-            st.markdown("### Summary")
-            st.write(st.session_state['english_summary'])
-        st.markdown("### Download Subtitles")
-        include_timeframe = st.checkbox("Include timeframe in subtitles", value=True)
-        transcript_to_download = st.session_state['primary_transcript'] or st.session_state['english_transcript']
-        if transcript_to_download:
-            srt_content = generate_srt(transcript_to_download, include_timeframe)
-            st.download_button(label="Download Subtitles (SRT)", data=srt_content, file_name="subtitles.srt", mime="text/plain")
-        st.markdown("### Edit Subtitles")
-        transcript_to_edit = st.session_state['primary_transcript'] or st.session_state['english_transcript']
-        if transcript_to_edit and st.button("Delete Subtitles"):
-            st.session_state['app_state'] = 'editing'
-            st.rerun()
-
-    if st.session_state['app_state'] == 'editing':
-        st.markdown("### Delete Subtitles")
-        transcript_to_edit = st.session_state['primary_transcript'] or st.session_state['english_transcript']
-        for i, (text, start, end) in enumerate(transcript_to_edit):
-            st.write(f"{i}: [{format_time(start)} - {format_time(end)}] {text}")
-        indices_input = st.text_input("Enter the indices of subtitles to delete (comma-separated, e.g., 0,1,3):")
-        if st.button("Confirm Deletion"):
-            try:
-                delete_indices = [int(idx.strip()) for idx in indices_input.split(',') if idx.strip()]
-                delete_indices = [idx for idx in delete_indices if 0 <= idx < len(transcript_to_edit)]
-                keep_indices = [i for i in range(len(transcript_to_edit)) if i not in delete_indices]
-                if not keep_indices:
-                    st.error("All subtitles are deleted. No video to generate.")
-                else:
-                    edited_video_path = create_edited_video(st.session_state['video_path'], transcript_to_edit, keep_indices)
-                    if edited_video_path:
-                        st.session_state['edited_video_path'] = edited_video_path
-                        st.session_state['app_state'] = 'results'
-                        st.rerun()
-            except ValueError:
-                st.error("Invalid input. Please enter comma-separated integers.")
-            except Exception as e:
-                st.error(f"Error during video editing: {str(e)}")
-        if st.button("Cancel Deletion"):
-            st.session_state['app_state'] = 'results'
-            st.rerun()
-
-    if st.session_state['app_state'] == 'results' and st.session_state['edited_video_path']:
-        st.markdown("### Edited Video")
-        st.video(st.session_state['edited_video_path'])
-        with open(st.session_state['edited_video_path'], "rb") as file:
-            st.download_button(label="Download Edited Video", data=file, file_name="edited_video.mp4", mime="video/mp4")
-
-    if st.session_state.get('video_path') and st.button("Reset"):
-        if st.session_state['video_path'] and os.path.exists(st.session_state['video_path']):
-            os.remove(st.session_state['video_path'])
-        if st.session_state['edited_video_path'] and os.path.exists(st.session_state['edited_video_path']):
-            os.remove(st.session_state['edited_video_path'])
-        st.session_state.clear()
-        st.rerun()
-
-if __name__ == "__main__":
+import streamlit as st
+import tempfile
+import os
+import torch
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq, AutoTokenizer, AutoModelForSeq2SeqLM
+import librosa
+import numpy as np
+import ffmpeg
+import time
+import json
+import psutil
+
+def format_time(seconds):
+    minutes = int(seconds // 60)
+    secs = int(seconds % 60)
+    return f"{minutes}:{secs:02d}"
+
+def seconds_to_srt_time(seconds):
+    hours = int(seconds // 3600)
+    minutes = int((seconds % 3600) // 60)
+    secs = int(seconds % 60)
+    millis = int((seconds - int(seconds)) * 1000)
+    return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}"
+
+@st.cache_resource
+def load_model(language='en', summarizer_type='bart'):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    if language == 'ur':
+        processor = AutoProcessor.from_pretrained("GogetaBlueMUI/whisper-medium-ur-fleurs")
+        model = AutoModelForSpeechSeq2Seq.from_pretrained("GogetaBlueMUI/whisper-medium-ur-fleurs").to(device)
+    else:
+        processor = AutoProcessor.from_pretrained("openai/whisper-small")
+        model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small").to(device)
+    if device.type == "cuda":
+        model = model.half()
+    if summarizer_type == 'bart':
+        sum_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
+        sum_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn").to(device)
+    else:
+        sum_tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-large-book-summary")
+        sum_model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-large-book-summary").to(device)
+    return processor, model, sum_tokenizer, sum_model, device
+
+def split_audio_into_chunks(audio, sr, chunk_duration):
+    chunk_samples = int(chunk_duration * sr)
+    chunks = [audio[start:start + chunk_samples] for start in range(0, len(audio), chunk_samples)]
+    return chunks
+
+def transcribe_audio(audio, sr, processor, model, device, start_time, language, task="transcribe"):
+    inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
+    input_features = inputs.input_features.to(device)
+    if model.dtype == torch.float16:
+        input_features = input_features.half()
+    generate_kwargs = {
+        "task": task,
+        "language": "urdu" if language == "ur" else language,
+        "max_new_tokens": 128,
+        "return_timestamps": True
+    }
+    try:
+        with torch.no_grad():
+            outputs = model.generate(input_features, **generate_kwargs)
+        text = processor.decode(outputs[0], skip_special_tokens=True)
+        return [(text, start_time, start_time + len(audio) / sr)]
+    except Exception as e:
+        st.error(f"Transcription error: {str(e)}")
+        return [(f"Error: {str(e)}", start_time, start_time + len(audio) / sr)]
+
+def process_chunks(chunks, sr, processor, model, device, language, chunk_duration, task="transcribe", transcript_file="temp_transcript.json"):
+    transcript = []
+    chunk_start = 0
+    total_chunks = len(chunks)
+    progress_bar = st.progress(0)
+    status_text = st.empty()
+    if os.path.exists(transcript_file):
+        os.remove(transcript_file)
+    for i, chunk in enumerate(chunks):
+        status_text.text(f"Processing chunk {i+1}/{total_chunks}...")
+        try:
+            memory = psutil.virtual_memory()
+            st.write(f"Memory usage: {memory.percent}% (Chunk {i+1}/{total_chunks})")
+            chunk_transcript = transcribe_audio(chunk, sr, processor, model, device, chunk_start, language, task)
+            transcript.extend(chunk_transcript)
+            with open(transcript_file, "w", encoding="utf-8") as f:
+                json.dump(transcript, f, ensure_ascii=False)
+            chunk_start += chunk_duration
+            progress_bar.progress((i + 1) / total_chunks)
+        except Exception as e:
+            st.error(f"Error processing chunk {i+1}: {str(e)}")
+            break
+    status_text.text("Processing complete!")
+    progress_bar.empty()
+    return transcript
+
+def summarize_text(text, tokenizer, model, device, summarizer_type='bart'):
+    if summarizer_type == 'bart':
+        max_input_length = 1024
+        max_summary_length = 150
+        chunk_size = 512
+    else:
+        max_input_length = 16384
+        max_summary_length = 512
+        chunk_size = 8192
+    inputs = tokenizer(text, return_tensors="pt", truncation=False)
+    input_ids = inputs["input_ids"].to(device)
+    num_tokens = input_ids.shape[1]
+    st.write(f"Number of tokens in input: {num_tokens}")
+    if num_tokens < 50:
+        return "Transcript too short to summarize effectively."
+    try:
+        summaries = []
+        if num_tokens <= max_input_length:
+            truncated_inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=max_input_length).to(device)
+            with torch.no_grad():
+                summary_ids = model.generate(truncated_inputs["input_ids"], num_beams=4, max_length=max_summary_length, min_length=50, early_stopping=True, temperature=0.7)
+            summaries.append(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
+        else:
+            st.write(f"Transcript exceeds {max_input_length} tokens. Processing in chunks...")
+            tokens = input_ids[0].tolist()
+            for i in range(0, num_tokens, chunk_size):
+                chunk_tokens = tokens[i:i + chunk_size]
+                chunk_input_ids = torch.tensor([chunk_tokens]).to(device)
+                with torch.no_grad():
+                    summary_ids = model.generate(chunk_input_ids, num_beams=4, max_length=max_summary_length // 2, min_length=25, early_stopping=True, temperature=0.7)
+                summaries.append(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
+            combined_summary = " ".join(summaries)
+            combined_inputs = tokenizer(combined_summary, return_tensors="pt", truncation=True, max_length=max_input_length).to(device)
+            with torch.no_grad():
+                final_summary_ids = model.generate(combined_inputs["input_ids"], num_beams=4, max_length=max_summary_length, min_length=50, early_stopping=True, temperature=0.7)
+            summaries = [tokenizer.decode(final_summary_ids[0], skip_special_tokens=True)]
+        return " ".join(summaries)
+    except Exception as e:
+        st.error(f"Summarization error: {str(e)}")
+        return f"Error: {str(e)}"
+
+def save_uploaded_file(uploaded_file):
+    try:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_file:
+            tmp_file.write(uploaded_file.read())
+            return tmp_file.name
+    except Exception as e:
+        st.error(f"Error saving uploaded file: {str(e)}")
+        return None
+
+def merge_intervals(intervals):
+    if not intervals:
+        return []
+    intervals.sort(key=lambda x: x[0])
+    merged = [intervals[0]]
+    for current in intervals[1:]:
+        previous = merged[-1]
+        if previous[1] >= current[0]:
+            merged[-1] = (previous[0], max(previous[1], current[1]))
+        else:
+            merged.append(current)
+    return merged
+
+def create_edited_video(video_path, transcript, keep_indices):
+    try:
+        intervals_to_keep = [(transcript[i][1], transcript[i][2]) for i in keep_indices]
+        merged_intervals = merge_intervals(intervals_to_keep)
+        temp_files = []
+        for j, (start, end) in enumerate(merged_intervals):
+            temp_file = f"temp_{j}.mp4"
+            ffmpeg.input(video_path, ss=start, to=end).output(temp_file, c='copy').run(overwrite_output=True, quiet=True)
+            temp_files.append(temp_file)
+        with open("list.txt", "w") as f:
+            for temp_file in temp_files:
+                f.write(f"file '{temp_file}'\n")
+        edited_video_path = "edited_video.mp4"
+        ffmpeg.input('list.txt', format='concat', safe=0).output(edited_video_path, c='copy').run(overwrite_output=True, quiet=True)
+        for temp_file in temp_files:
+            if os.path.exists(temp_file):
+                os.remove(temp_file)
+        if os.path.exists("list.txt"):
+            os.remove("list.txt")
+        return edited_video_path
+    except Exception as e:
+        st.error(f"Error creating edited video: {str(e)}")
+        return None
+
+def generate_srt(transcript, include_timeframe=True):
+    srt_content = ""
+    for idx, (text, start, end) in enumerate(transcript, 1):
+        if include_timeframe:
+            start_time = seconds_to_srt_time(start)
+            end_time = seconds_to_srt_time(end)
+            srt_content += f"{idx}\n{start_time} --> {end_time}\n{text}\n\n"
+        else:
+            srt_content += f"{text}\n\n"
+    return srt_content
+
+def main():
+    st.title("Video Transcription and Summarization App")
+    st.markdown("Upload a video to transcribe its audio, generate a summary, and edit subtitles.")
+
+    # Inject CSS to set video height
+    st.markdown("""
+        <style>
+        video {
+            width: 350px !important;
+            height: 500px !important;
+            object-fit: contain;
+        }
+        </style>
+    """, unsafe_allow_html=True)
+
+    if 'app_state' not in st.session_state:
+        st.session_state['app_state'] = 'upload'
+    if 'video_path' not in st.session_state:
+        st.session_state['video_path'] = None
+    if 'primary_transcript' not in st.session_state:
+        st.session_state['primary_transcript'] = None
+    if 'english_transcript' not in st.session_state:
+        st.session_state['english_transcript'] = None
+    if 'english_summary' not in st.session_state:
+        st.session_state['english_summary'] = None
+    if 'language' not in st.session_state:
+        st.session_state['language'] = None
+    if 'language_code' not in st.session_state:
+        st.session_state['language_code'] = None
+    if 'translate_to_english' not in st.session_state:
+        st.session_state['translate_to_english'] = False
+    if 'summarizer_type' not in st.session_state:
+        st.session_state['summarizer_type'] = None
+    if 'summary_generated' not in st.session_state:
+        st.session_state['summary_generated'] = False
+    if 'current_time' not in st.session_state:
+        st.session_state['current_time'] = 0
+    if 'edited_video_path' not in st.session_state:
+        st.session_state['edited_video_path'] = None
+    if 'search_query' not in st.session_state:
+        st.session_state['search_query'] = ""
+    if 'show_timeframe' not in st.session_state:
+        st.session_state['show_timeframe'] = True
+
+    st.write(f"Current app state: {st.session_state['app_state']}")
+
+    if st.session_state['app_state'] == 'upload':
+        with st.form(key="upload_form"):
+            uploaded_file = st.file_uploader("Upload a video", type=["mp4"])
+            if st.form_submit_button("Upload") and uploaded_file:
+                video_path = save_uploaded_file(uploaded_file)
+                if video_path:
+                    st.session_state['video_path'] = video_path
+                    st.session_state['app_state'] = 'processing'
+                    st.write(f"Uploaded file: {uploaded_file.name}")
+                    st.rerun()
+
+    if st.session_state['app_state'] == 'processing':
+        with st.form(key="processing_form"):
+            language = st.selectbox("Select language", ["English", "Urdu"], key="language_select")
+            language_code = "en" if language == "English" else "ur"
+            st.session_state['language'] = language
+            st.session_state['language_code'] = language_code
+            chunk_duration = st.number_input("Duration per chunk (seconds):", min_value=1.0, step=0.1, value=10.0)
+            if language_code == "ur":
+                translate_to_english = st.checkbox("Generate English translation", key="translate_checkbox")
+                st.session_state['translate_to_english'] = translate_to_english
+            else:
+                st.session_state['translate_to_english'] = False
+            if st.form_submit_button("Process"):
+                with st.spinner("Processing video..."):
+                    start_time = time.time()
+                    try:
+                        st.write("Extracting audio...")
+                        audio_path = "processed_audio.wav"
+                        ffmpeg.input(st.session_state['video_path']).output(audio_path, ac=1, ar=16000).run(overwrite_output=True, quiet=True)
+                        audio, sr = librosa.load(audio_path, sr=16000)
+                        audio = np.nan_to_num(audio, nan=0.0, posinf=0.0, neginf=0.0)
+                        audio_duration = len(audio) / sr
+                        st.write(f"Audio duration: {audio_duration:.2f} seconds")
+                        if audio_duration < 5:
+                            st.error("Audio too short (< 5s). Upload a longer video.")
+                            return
+                        summarizer_type = 'bart' if audio_duration <= 300 else 'led'
+                        st.write(f"Using summarizer: {summarizer_type}")
+                        st.session_state['summarizer_type'] = summarizer_type
+                        st.write("Loading models...")
+                        processor, model, sum_tokenizer, sum_model, device = load_model(language_code, summarizer_type)
+                        st.write("Splitting audio into chunks...")
+                        chunks = split_audio_into_chunks(audio, sr, chunk_duration)
+                        st.write(f"Number of chunks: {len(chunks)}")
+                        st.write("Transcribing audio...")
+                        primary_transcript = process_chunks(chunks, sr, processor, model, device, language_code, chunk_duration, task="transcribe", transcript_file="temp_primary_transcript.json")
+                        english_transcript = None
+                        if st.session_state['translate_to_english'] and language_code == "ur":
+                            st.write("Translating to English...")
+                            processor, model, _, _, device = load_model('en', summarizer_type)
+                            english_transcript = process_chunks(chunks, sr, processor, model, device, 'ur', chunk_duration, task="translate", transcript_file="temp_english_transcript.json")
+                        st.session_state.update({
+                            'primary_transcript': primary_transcript,
+                            'english_transcript': english_transcript,
+                            'summary_generated': False,
+                            'app_state': 'results'
+                        })
+                        st.write("Processing completed successfully!")
+                        st.rerun()
+                    except Exception as e:
+                        st.error(f"Processing failed: {str(e)}")
+                    finally:
+                        if os.path.exists(audio_path):
+                            os.remove(audio_path)
+                        for temp_file in ["temp_primary_transcript.json", "temp_english_transcript.json"]:
+                            if os.path.exists(temp_file):
+                                os.remove(temp_file)
+
+    if st.session_state['app_state'] == 'results':
+        st.video(st.session_state['video_path'], start_time=st.session_state['current_time'])
+        st.session_state['show_timeframe'] = st.checkbox("Show timeframe in transcript", value=st.session_state['show_timeframe'])
+        st.markdown("### Search Subtitles")
+        search_query = st.text_input("Search subtitles...", value=st.session_state['search_query'] or "", key="search_input")
+        st.session_state['search_query'] = search_query.lower()
+        st.markdown(f"### {st.session_state['language']} Transcript")
+        for text, start, end in st.session_state['primary_transcript']:
+            display_text = text.lower()
+            if not search_query or search_query in display_text:
+                label = f"[{format_time(start)} - {format_time(end)}] {text}" if st.session_state['show_timeframe'] else text
+                if st.button(label, key=f"primary_{start}"):
+                    st.session_state['current_time'] = start
+                    st.rerun()
+        if st.session_state['english_transcript']:
+            st.markdown("### English Translation")
+            for text, start, end in st.session_state['english_transcript']:
+                display_text = text.lower()
+                if not search_query or search_query in display_text:
+                    label = f"[{format_time(start)} - {format_time(end)}] {text}" if st.session_state['show_timeframe'] else text
+                    if st.button(label, key=f"english_{start}"):
+                        st.session_state['current_time'] = start
+                        st.rerun()
+        if (st.session_state['language_code'] == 'en' or st.session_state['translate_to_english']) and not st.session_state['summary_generated']:
+            if st.button("Generate Summary"):
+                with st.spinner("Generating summary..."):
+                    try:
+                        _, _, sum_tokenizer, sum_model, device = load_model(st.session_state['language_code'], st.session_state['summarizer_type'])
+                        full_text = " ".join([text for text, _, _ in (st.session_state['english_transcript'] or st.session_state['primary_transcript'])])
+                        english_summary = summarize_text(full_text, sum_tokenizer, sum_model, device, st.session_state['summarizer_type'])
+                        st.session_state['english_summary'] = english_summary
+                        st.session_state['summary_generated'] = True
+                    except Exception as e:
+                        st.error(f"Summary generation failed: {str(e)}")
+        if st.session_state['english_summary'] and st.session_state['summary_generated']:
+            st.markdown("### Summary")
+            st.write(st.session_state['english_summary'])
+        st.markdown("### Download Subtitles")
+        include_timeframe = st.checkbox("Include timeframe in subtitles", value=True)
+        transcript_to_download = st.session_state['primary_transcript'] or st.session_state['english_transcript']
+        if transcript_to_download:
+            srt_content = generate_srt(transcript_to_download, include_timeframe)
+            st.download_button(label="Download Subtitles (SRT)", data=srt_content, file_name="subtitles.srt", mime="text/plain")
+        st.markdown("### Edit Subtitles")
+        transcript_to_edit = st.session_state['primary_transcript'] or st.session_state['english_transcript']
+        if transcript_to_edit and st.button("Delete Subtitles"):
+            st.session_state['app_state'] = 'editing'
+            st.rerun()
+
+    if st.session_state['app_state'] == 'editing':
+        st.markdown("### Delete Subtitles")
+        transcript_to_edit = st.session_state['primary_transcript'] or st.session_state['english_transcript']
+        for i, (text, start, end) in enumerate(transcript_to_edit):
+            st.write(f"{i}: [{format_time(start)} - {format_time(end)}] {text}")
+        indices_input = st.text_input("Enter the indices of subtitles to delete (comma-separated, e.g., 0,1,3):")
+        if st.button("Confirm Deletion"):
+            try:
+                delete_indices = [int(idx.strip()) for idx in indices_input.split(',') if idx.strip()]
+                delete_indices = [idx for idx in delete_indices if 0 <= idx < len(transcript_to_edit)]
+                keep_indices = [i for i in range(len(transcript_to_edit)) if i not in delete_indices]
+                if not keep_indices:
+                    st.error("All subtitles are deleted. No video to generate.")
+                else:
+                    edited_video_path = create_edited_video(st.session_state['video_path'], transcript_to_edit, keep_indices)
+                    if edited_video_path:
+                        st.session_state['edited_video_path'] = edited_video_path
+                        st.session_state['app_state'] = 'results'
+                        st.rerun()
+            except ValueError:
+                st.error("Invalid input. Please enter comma-separated integers.")
+            except Exception as e:
+                st.error(f"Error during video editing: {str(e)}")
+        if st.button("Cancel Deletion"):
+            st.session_state['app_state'] = 'results'
+            st.rerun()
+
+    if st.session_state['app_state'] == 'results' and st.session_state['edited_video_path']:
+        st.markdown("### Edited Video")
+        st.video(st.session_state['edited_video_path'])
+        with open(st.session_state['edited_video_path'], "rb") as file:
+            st.download_button(label="Download Edited Video", data=file, file_name="edited_video.mp4", mime="video/mp4")
+
+    if st.session_state.get('video_path') and st.button("Reset"):
+        if st.session_state['video_path'] and os.path.exists(st.session_state['video_path']):
+            os.remove(st.session_state['video_path'])
+        if st.session_state['edited_video_path'] and os.path.exists(st.session_state['edited_video_path']):
+            os.remove(st.session_state['edited_video_path'])
+        st.session_state.clear()
+        st.rerun()
+
+if __name__ == "__main__":
     main()