Update app.py

app.py

@@ -1,3 +1,73 @@
-import gradio as gr
+import av
+import torch
+import numpy as np
+from fastapi import FastAPI, UploadFile, File
+from transformers import AutoProcessor, AutoModel
+from huggingface_hub import hf_hub_download
 
-gr.Interface.load("models/microsoft/xclip-base-patch32").launch()
+app = FastAPI()
+
+np.random.seed(0)
+
+def read_video_pyav(container, indices):
+    '''
+    Decode the video with PyAV decoder.
+    Args:
+        container (`av.container.input.InputContainer`): PyAV container.
+        indices (`List[int]`): List of frame indices to decode.
+    Returns:
+        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
+    '''
+    frames = []
+    container.seek(0)
+    start_index = indices[0]
+    end_index = indices[-1]
+    for i, frame in enumerate(container.decode(video=0)):
+        if i > end_index:
+            break
+        if i >= start_index and i in indices:
+            frames.append(frame)
+    return np.stack([x.to_ndarray(format="rgb24") for x in frames])
+
+def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
+    '''
+    Sample a given number of frame indices from the video.
+    Args:
+        clip_len (`int`): Total number of frames to sample.
+        frame_sample_rate (`int`): Sample every n-th frame.
+        seg_len (`int`): Maximum allowed index of sample's last frame.
+    Returns:
+        indices (`List[int]`): List of sampled frame indices
+    '''
+    converted_len = int(clip_len * frame_sample_rate)
+    end_idx = np.random.randint(converted_len, seg_len)
+    start_idx = end_idx - converted_len
+    indices = np.linspace(start_idx, end_idx, num=clip_len)
+    indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
+    return indices
+
+processor = AutoProcessor.from_pretrained("microsoft/xclip-base-patch32")
+model = AutoModel.from_pretrained("microsoft/xclip-base-patch32")
+
+@app.post("/classify_video/")
+async def classify_video(file: UploadFile):
+    file_bytes = await file.read()
+    
+    container = av.open(file_bytes)
+    indices = sample_frame_indices(clip_len=8, frame_sample_rate=1, seg_len=container.streams.video[0].frames)
+    video = read_video_pyav(container, indices)
+
+    inputs = processor(
+        text=["playing sports", "eating spaghetti", "go shopping"],
+        videos=[video],  # Changed list(video) to [video] to avoid error
+        return_tensors="pt",
+        padding=True,
+    )
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits_per_video = outputs.logits_per_video
+    probs = logits_per_video.softmax(dim=1)
+    
+    return {"classification_probabilities": probs.tolist()}