Update app.py

app.py

@@ -1,127 +1,22 @@
-import math
-from io import BytesIO
 import gradio as gr
 import cv2
-import os
-import requests
-from pydub import AudioSegment
-from faster_whisper import WhisperModel
-
-model = WhisperModel("small", device="cpu", compute_type="int8")
-
-API_KEY = os.getenv("API_KEY")
-
-FACE_API_URL = "https://api-inference.huggingface.co/models/dima806/facial_emotions_image_detection"
-TEXT_API_URL = "https://api-inference.huggingface.co/models/SamLowe/roberta-base-go_emotions"
-headers = {"Authorization": "Bearer " + API_KEY + ""}
-
-
-def extract_frames(video_path):
-    cap = cv2.VideoCapture(video_path)
+import moviepy.editor as mpe
+from moviepy.editor import VideoFileClip
+
+def process(video_path):
+    print(video_path)
+    
+    clip = mpe.VideoFileClip(video_path)
+    clip.write_videofile('mp4file.mp4', fps=60)
+    
+    cap = cv2.VideoCapture('mp4file.mp4')
     fps = int(cap.get(cv2.CAP_PROP_FPS))
     total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    interval = fps
-    result = []
-
-    for i in range(0, total_frames, interval):
-        cap.set(cv2.CAP_PROP_POS_FRAMES, i)
-        ret, frame = cap.read()
-        if ret:
-            _, img_encoded = cv2.imencode('.jpg', frame)
-            img_bytes = img_encoded.tobytes()
-
-            response = requests.post(FACE_API_URL, headers=headers, data=img_bytes)
-            result.append({item['label']: item['score'] for item in response.json()})
-
-    print("Frame extraction completed.")
-
-    cap.release()
-    print(result)
-    return result
-
-
-def analyze_sentiment(text):
-    response = requests.post(TEXT_API_URL, headers=headers, json=text)
-    print(response.json())
-    sentiment_list = response.json()[0]
-    print(sentiment_list)
-    sentiment_results = {result['label']: result['score'] for result in sentiment_list}
-    return sentiment_results
-
-
-def video_to_audio(input_video):
-    audio = AudioSegment.from_file(input_video)
-    audio_binary = audio.export(format="wav").read()
-    audio_bytesio = BytesIO(audio_binary)
-
-    segments, info = model.transcribe(audio_bytesio, beam_size=5)
-
-    print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
-
-    frames_sentiments = extract_frames(input_video)
-
-    transcript = ''
-    final_output = []
-    for segment in segments:
-        transcript = transcript + segment.text + " "
-        print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
-        transcript_segment_sentiment = analyze_sentiment(segment.text)
-
-        emotion_totals = {
-            'admiration': 0.0,
-            'amusement': 0.0,
-            'angry': 0.0,
-            'annoyance': 0.0,
-            'approval': 0.0,
-            'caring': 0.0,
-            'confusion': 0.0,
-            'curiosity': 0.0,
-            'desire': 0.0,
-            'disappointment': 0.0,
-            'disapproval': 0.0,
-            'disgust': 0.0,
-            'embarrassment': 0.0,
-            'excitement': 0.0,
-            'fear': 0.0,
-            'gratitude': 0.0,
-            'grief': 0.0,
-            'happy': 0.0,
-            'love': 0.0,
-            'nervousness': 0.0,
-            'optimism': 0.0,
-            'pride': 0.0,
-            'realization': 0.0,
-            'relief': 0.0,
-            'remorse': 0.0,
-            'sad': 0.0,
-            'surprise': 0.0,
-            'neutral': 0.0
-        }
-
-        counter = 0
-        for i in range(math.ceil(segment.start), math.floor(segment.end)):
-            for emotion in frames_sentiments[i].keys():
-                emotion_totals[emotion] += frames_sentiments[i].get(emotion)
-            counter += 1
-
-        for emotion in emotion_totals:
-            emotion_totals[emotion] /= counter
-
-        video_segment_sentiment = emotion_totals
-
-        segment_finals = {segment.id: (segment.text, segment.start, segment.end, transcript_segment_sentiment,
-                                       video_segment_sentiment)}
-        final_output.append(segment_finals)
-        print(segment_finals)
-        print(final_output)
-
-    print(final_output)
-
-    return final_output
+    interval = int(fps/2)
+    print(interval, total_frames)
+    return interval, total_frames
 
+demo = gr.Interface(fn=process, inputs=gr.Video(format='mp4'), outputs=["textbox", "textbox"], title="Video Frame Counter")
 
-gr.Interface(
-    fn=video_to_audio,
-    inputs=gr.Video(sources=["upload"]),
-    outputs=gr.Textbox()
-).launch()
+if __name__ == "__main__":
+    demo.launch()