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Dockerfile

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+FROM python:3.11
+
+# Add a user
+RUN useradd -m -u 1000 user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+# Install system packages
+COPY packages.txt /tmp/packages.txt
+USER root
+RUN apt-get update && \
+    xargs -a /tmp/packages.txt apt-get install -y && \
+    apt-get clean && rm -rf /var/lib/apt/lists/*
+USER user
+
+# Set working directory
+WORKDIR /app
+
+# Install Python packages
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy the rest of the code
+COPY --chown=user . /app
+
+# Run the app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

ModelCode.py

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+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
+
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+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}