Delete ModelCode.py

ModelCode.py

@@ -1,85 +0,0 @@
-import os
-import torch
-from transformers import CLIPProcessor, CLIPModel
-from PIL import Image
-import torchvision.transforms as transforms
-from pytesseract import image_to_string
-import cv2
-from transformers import BlipProcessor, BlipForConditionalGeneration
-from collections import Counter
-from pytesseract import pytesseract
-
-clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-pytesseract.pytesseract.tesseract_cmd = "/usr/bin/tesseract"
-
-blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-clip_model = clip_model.to(device)
-blip_model = blip_model.to(device)
-
-def extract_frames(video_path, frame_rate=1):
-    cap = cv2.VideoCapture(video_path)
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    frames = []
-    count = 0
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        if int(count % (fps * frame_rate)) == 0:
-            img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-            frames.append(img)
-        count += 1
-
-    cap.release()
-    return frames
-
-def classify_frame_with_clip(image):
-    texts = ["Ayurveda", "Non-Ayurveda"]
-    inputs = clip_processor(text=texts, images=image, return_tensors="pt", padding=True).to(device)
-    outputs = clip_model(**inputs)
-    logits_per_image = outputs.logits_per_image
-    probs = logits_per_image.softmax(dim=1)
-    pred = torch.argmax(probs, dim=1).item()
-    return texts[pred]
-
-def get_caption_with_blip(image):
-    inputs = blip_processor(images=image, return_tensors="pt").to(device)
-    out = blip_model.generate(**inputs)
-    caption = blip_processor.decode(out[0], skip_special_tokens=True)
-    return caption
-
-def extract_text_with_ocr(image):
-    return image_to_string(image)
-
-def classify_video(video_path):
-    frames = extract_frames(video_path, frame_rate=2)
-
-    clip_preds = []
-    blip_preds = []
-    ocr_preds = []
-
-    for frame in frames:
-        clip_result = classify_frame_with_clip(frame)
-        clip_preds.append(clip_result)
-
-        caption = get_caption_with_blip(frame)
-        blip_input = clip_processor(text=["Ayurveda", "Non-Ayurveda"], images=frame, return_tensors="pt", padding=True).to(device)
-        blip_output = clip_model(**blip_input)
-        blip_probs = blip_output.logits_per_image.softmax(dim=1)
-        blip_pred = torch.argmax(blip_probs, dim=1).item()
-        blip_preds.append(["Ayurveda", "Non-Ayurveda"][blip_pred])
-
-        text = extract_text_with_ocr(frame)
-        if any(keyword in text.lower() for keyword in ["ayurveda", "herbal", "vedic", "naturopathy"]):
-            ocr_preds.append("Ayurveda")
-        else:
-            ocr_preds.append("Non-Ayurveda")
-
-    all_preds = clip_preds + blip_preds + ocr_preds
-    final_pred = Counter(all_preds).most_common(1)[0][0]
-    return {"Type": final_pred}