Upload 14 files

.env

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+API_KEY=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

.gitignore

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+.env

.gradio/certificate.pem

@@ -0,0 +1,31 @@
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

README.md

@@ -1,12 +1,2 @@
----
-title: Unicorm
-emoji: 🌍
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 5.34.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# EXP-Unicorn-Hack7
+เราจะไปตั้งหัวแถวใหม่กันครับ

backend/app.py

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+import pandas as pd
+import cv2
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+from transformers import AutoImageProcessor, TimesformerForVideoClassification
+import os
+import logging
+from fastapi import FastAPI, File, UploadFile, HTTPException, BackgroundTasks
+from pydantic import BaseModel
+from typing import List, Dict
+import joblib
+import torch.nn as nn
+import tempfile
+from datetime import datetime
+import torch.optim as optim
+from torch.cuda.amp import autocast
+import hashlib
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Configuration
+CONFIG = {
+    'img_height': 224,
+    'img_width': 224,
+    'num_frames': 4,  # Reduced from 8 to 4
+    'window_seconds': 3.0,
+    'cache_dir': './preprocessed_cache',
+    'feedback_dir': './feedback_videos',
+    'feedback_csv': 'feedback.csv',
+}
+
+# Action columns
+ACTION_COLS = ['Advancing', 'Retreating', 'Enclosing', 'Spreading', 'Rising',
+               'Descending', 'Directing', 'Indirecting', 'Increasing Pressure',
+               'Decreasing Pressure', 'Acceleration', 'Decelerating']
+
+# Action pairs for constraint enforcement
+action_pairs = [
+    ('Advancing', 'Retreating'),
+    ('Enclosing', 'Spreading'),
+    ('Rising', 'Descending'),
+    ('Directing', 'Indirecting'),
+    ('Increasing Pressure', 'Decreasing Pressure'),
+    ('Acceleration', 'Decelerating')
+]
+
+# Model definition
+class TwoStreamTimeSformer(nn.Module):
+    def __init__(self, num_labels):
+        super(TwoStreamTimeSformer, self).__init__()
+        self.rgb_model = TimesformerForVideoClassification.from_pretrained(
+            "facebook/timesformer-base-finetuned-k400", num_labels=num_labels, ignore_mismatched_sizes=True
+        )
+        self.flow_model = TimesformerForVideoClassification.from_pretrained(
+            "facebook/timesformer-base-finetuned-k400", num_labels=num_labels, ignore_mismatched_sizes=True
+        )
+        self.fusion = nn.Linear(num_labels * 2, num_labels)
+
+    def forward(self, rgb_inputs, flow_inputs):
+        rgb_logits = self.rgb_model(rgb_inputs).logits
+        flow_logits = self.flow_model(flow_inputs).logits
+        fused = torch.cat([rgb_logits, flow_logits], dim=-1)
+        return self.fusion(fused)
+
+def parse_timestamp(time_str):
+    try:
+        time_str = time_str.strip()
+        if len(time_str) < 4:
+            time_str = '0' * (4 - len(time_str)) + time_str
+        minutes = int(time_str[:-2])
+        seconds = int(time_str[-2:])
+        return minutes * 60 + seconds
+    except ValueError as e:
+        logger.error(f"Invalid timestamp format: {time_str}")
+        raise ValueError(f"Invalid timestamp format: {time_str}")
+
+def get_video_hash(video_bytes):
+    return hashlib.md5(video_bytes).hexdigest()
+
+def preprocess_video(video_bytes, timestamp_seconds, image_processor, num_frames=CONFIG['num_frames'], window_seconds=CONFIG['window_seconds']):
+    # Save video bytes to a temporary file
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp:
+        tmp.write(video_bytes)
+        video_path = tmp.name
+
+    try:
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            logger.error(f"Failed to open video at {video_path}")
+            raise ValueError("Cannot open video file")
+
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        duration = frame_count / fps if fps > 0 else 0
+        if timestamp_seconds < 0 or timestamp_seconds > duration:
+            logger.warning(f"Timestamp {timestamp_seconds}s out of range for video duration {duration}s")
+            raise ValueError(f"Timestamp {timestamp_seconds}s out of range (duration: {duration}s)")
+
+        center_frame = int(timestamp_seconds * fps)
+        window_frames = int(window_seconds * fps)
+        start_frame = max(0, center_frame - window_frames // 2)
+        end_frame = min(frame_count - 1, center_frame + window_frames // 2)
+        frames_rgb = []
+        frames_flow = []
+        target_frames = np.linspace(start_frame, end_frame, num_frames, dtype=int).tolist()
+
+        prev_gray = None
+        all_flows = []
+        for idx in target_frames:
+            cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
+            ret, frame = cap.read()
+            if ret:
+                frame = cv2.resize(frame, (CONFIG['img_width'], CONFIG['img_height']))
+                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                frames_rgb.append(frame_rgb)
+                gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                if prev_gray is not None:
+                    flow = cv2.calcOpticalFlowFarneback(prev_gray, gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
+                    flow = cv2.normalize(flow, None, 0, 255, cv2.NORM_MINMAX).astype(np.float32) / 255.0
+                    all_flows.append(flow)
+                prev_gray = gray
+            else:
+                logger.warning(f"Failed to read frame {idx}")
+
+        # Pad if necessary
+        if len(frames_rgb) < num_frames:
+            frames_rgb.extend([frames_rgb[-1].copy() if frames_rgb else np.zeros((CONFIG['img_height'], CONFIG['img_width'], 3), dtype=np.uint8)] * (num_frames - len(frames_rgb)))
+            all_flows.extend([np.zeros((CONFIG['img_height'], CONFIG['img_width'], 2), dtype=np.float32)] * (num_frames - len(all_flows)))
+
+        frames_rgb = frames_rgb[:num_frames]
+        frames_flow = np.stack(all_flows, axis=0) if all_flows else np.zeros((num_frames, CONFIG['img_height'], CONFIG['img_width'], 2), dtype=np.float32)
+        if len(frames_flow) < num_frames:
+            frames_flow = np.pad(frames_flow, ((0, num_frames - len(frames_flow)), (0, 0), (0, 0), (0, 0)), mode='edge')
+        frames_flow = frames_flow[:num_frames]
+
+        inputs_rgb = image_processor(frames_rgb, return_tensors="pt")['pixel_values'].squeeze(0)
+
+        flow_magnitude = np.linalg.norm(frames_flow, axis=3)
+        flow_angle = np.arctan2(frames_flow[:, :, :, 1], frames_flow[:, :, :, 0])
+        frames_flow = np.stack([flow_magnitude, flow_angle, np.zeros_like(flow_magnitude)], axis=1)
+        frames_flow = np.clip(frames_flow, 0, 1) * 255.0
+        frames_flow = frames_flow.astype(np.uint8)
+
+        flow_images = [frames_flow[i].transpose(1, 2, 0) for i in range(frames_flow.shape[0])]
+        inputs_flow = image_processor(flow_images, return_tensors="pt")['pixel_values'].squeeze(0)
+
+        return inputs_rgb, inputs_flow
+
+    finally:
+        cap.release()
+        os.unlink(video_path)
+
+async def preprocess_video_async(video_bytes, timestamp_seconds, image_processor, cache_key):
+    cache_path = os.path.join(CONFIG['cache_dir'], f"{cache_key}.pkl")
+    os.makedirs(CONFIG['cache_dir'], exist_ok=True)
+    
+    if os.path.exists(cache_path):
+        logger.info(f"Loading cached inputs from {cache_path}")
+        return joblib.load(cache_path)
+    
+    inputs_rgb, inputs_flow = preprocess_video(video_bytes, timestamp_seconds, image_processor)
+    joblib.dump((inputs_rgb, inputs_flow), cache_path)
+    logger.info(f"Saved preprocessed inputs to {cache_path}")
+    return inputs_rgb, inputs_flow
+
+def check_label_constraints(preds, action_pairs, action_cols):
+    violations = []
+    for action1, action2 in action_pairs:
+        idx1 = action_cols.index(action1)
+        idx2 = action_cols.index(action2)
+        val1 = preds[idx1]
+        val2 = preds[idx2]
+        if val1 == 1 and val2 == 1:
+            violations.append((action1, action2, val1, val2))
+    return violations
+
+def enforce_constraints(preds, probs, action_pairs, action_cols):
+    adjusted_preds = preds.copy()
+    violations = check_label_constraints(preds, action_pairs, action_cols)
+    for action1, action2, _, _ in violations:
+        idx1 = action_cols.index(action1)
+        idx2 = action_cols.index(action2)
+        prob1 = probs[idx1]
+        prob2 = probs[idx2]
+        if prob1 >= prob2:
+            adjusted_preds[idx2] = 0
+        else:
+            adjusted_preds[idx1] = 0
+    return adjusted_preds
+
+def validate_feedback_actions(actions: Dict[str, int], action_pairs, action_cols):
+    violations = []
+    for action1, action2 in action_pairs:
+        idx1 = action_cols.index(action1)
+        idx2 = action_cols.index(action2)
+        val1 = actions.get(action1, 0)
+        val2 = actions.get(action2, 0)
+        if val1 == 1 and val2 == 1:
+            violations.append(f"Invalid: Both {action1} and {action2} are 1")
+    return violations
+
+def save_feedback(timestamp: str, actions: Dict[str, int], probabilities: List[float], video_bytes: bytes):
+    os.makedirs(CONFIG['feedback_dir'], exist_ok=True)
+    video_filename = f"feedback_{timestamp}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp4"
+    video_path = os.path.join(CONFIG['feedback_dir'], video_filename)
+    with open(video_path, 'wb') as f:
+        f.write(video_bytes)
+    
+    feedback_data = {
+        'timestamp': timestamp,
+        'video_path': video_path,
+        **{f"action_{k}": v for k, v in actions.items()},
+        **{f"prob_{k}": p for k, p in zip(ACTION_COLS, probabilities)}
+    }
+    
+    df = pd.DataFrame([feedback_data])
+    if os.path.exists(CONFIG['feedback_csv']):
+        df.to_csv(CONFIG['feedback_csv'], mode='a', header=False, index=False)
+    else:
+        df.to_csv(CONFIG['feedback_csv'], mode='w', header=True, index=False)
+    
+    logger.info(f"Saved feedback for timestamp {timestamp} to {CONFIG['feedback_csv']} and video to {video_path}")
+
+class FeedbackDataset(Dataset):
+    def __init__(self, feedback_csv, image_processor):
+        self.feedback_df = pd.read_csv(feedback_csv)
+        self.image_processor = image_processor
+        self.action_cols = [f"action_{col}" for col in ACTION_COLS]
+
+    def __len__(self):
+        return len(self.feedback_df)
+
+    def __getitem__(self, idx):
+        row = self.feedback_df.iloc[idx]
+        video_path = row['video_path']
+        timestamp = parse_timestamp(row['timestamp'])
+        labels = row[self.action_cols].values.astype(np.float32)
+        
+        with open(video_path, 'rb') as f:
+            video_bytes = f.read()
+        
+        rgb_inputs, flow_inputs = preprocess_video(video_bytes, timestamp, self.image_processor)
+        
+        return {
+            'rgb_inputs': rgb_inputs,
+            'flow_inputs': flow_inputs,
+            'labels': torch.tensor(labels, dtype=torch.float32)
+        }
+
+def retrain_model(model, feedback_csv, original_train_csv, device, num_epochs=1, batch_size=4):
+    image_processor = AutoImageProcessor.from_pretrained("facebook/timesformer-base-finetuned-k400")
+    feedback_dataset = FeedbackDataset(feedback_csv, image_processor)
+    feedback_loader = torch.utils.data.DataLoader(feedback_dataset, batch_size=batch_size, shuffle=True)
+    
+    original_dataset = FeedbackDataset(original_train_csv, image_processor)
+    original_loader = torch.utils.data.DataLoader(original_dataset, batch_size=batch_size, shuffle=True)
+    
+    optimizer = optim.Adam(model.parameters(), lr=1e-5)
+    criterion = nn.BCEWithLogitsLoss()
+    
+    model.train()
+    for epoch in range(num_epochs):
+        total_loss = 0
+        for loader in [feedback_loader, original_loader]:
+            for batch in loader:
+                rgb_inputs = batch['rgb_inputs'].to(device)
+                flow_inputs = batch['flow_inputs'].to(device)
+                labels = batch['labels'].to(device)
+                
+                optimizer.zero_grad()
+                with autocast():
+                    outputs = model(rgb_inputs, flow_inputs)
+                    loss = criterion(outputs, labels)
+                loss.backward()
+                optimizer.step()
+                
+                total_loss += loss.item()
+        
+        avg_loss = total_loss / (len(feedback_loader) + len(original_loader))
+        logger.info(f"Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_loss:.4f}")
+    
+    torch.save(model.state_dict(), "models/final_two_stream_timesformer_updated.pth")
+    logger.info("Saved updated model weights to final_two_stream_timesformer_updated.pth")
+
+# Initialize FastAPI app
+app = FastAPI(title="Action Recognition API")
+
+# Load model and metadata
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = TwoStreamTimeSformer(num_labels=len(ACTION_COLS))
+model.load_state_dict(torch.load("models/final_two_stream_timesformer.pth", map_location=device))
+model.to(device)
+model.eval()
+
+metadata = joblib.load("models/model_metadata.pkl")
+image_processor = AutoImageProcessor.from_pretrained("facebook/timesformer-base-finetuned-k400")
+
+# Request and response models
+class PredictionRequest(BaseModel):
+    timestamp: str
+
+class PredictionResponse(BaseModel):
+    timestamp: str
+    actions: Dict[str, int]
+    probabilities: List[float]
+
+class FeedbackRequest(BaseModel):
+    timestamp: str
+    actions: Dict[str, int]
+    probabilities: List[float]
+
+class FeedbackResponse(BaseModel):
+    status: str
+    message: str
+
+@app.post("/predict", response_model=PredictionResponse)
+async def predict(background_tasks: BackgroundTasks, file: UploadFile = File(...), timestamp: str = "0000"):
+    if not file.filename.endswith(('.mp4', '.avi', '.mov')):
+        raise HTTPException(status_code=400, detail="Invalid file format. Only .mp4, .avi, .mov are supported.")
+
+    try:
+        video_bytes = await file.read()
+        timestamp_seconds = parse_timestamp(timestamp)
+        cache_key = f"{get_video_hash(video_bytes)}_{timestamp}"
+
+        rgb_inputs, flow_inputs = await preprocess_video_async(video_bytes, timestamp_seconds, image_processor, cache_key)
+
+        with torch.no_grad():
+            rgb_inputs = rgb_inputs.unsqueeze(0).to(device, non_blocking=True)
+            flow_inputs = flow_inputs.unsqueeze(0).to(device, non_blocking=True)
+            with autocast():
+                outputs = model(rgb_inputs, flow_inputs)
+            probs = torch.sigmoid(outputs).cpu().numpy()[0]
+            preds = (probs > 0.7).astype(np.int32)
+            logger.info(f"Initial predictions (threshold 0.7): {dict(zip(ACTION_COLS, preds))}")
+            logger.info(f"Probabilities: {dict(zip(ACTION_COLS, probs))}")
+
+        adjusted_preds = enforce_constraints(preds, probs, action_pairs, ACTION_COLS)
+        logger.info(f"Adjusted predictions (after constraints): {dict(zip(ACTION_COLS, adjusted_preds))}")
+
+        actions_dict = {action: int(adjusted_preds[i]) for i, action in enumerate(ACTION_COLS)}
+        probabilities = probs.tolist()
+
+        return PredictionResponse(
+            timestamp=timestamp,
+            actions=actions_dict,
+            probabilities=probabilities
+        )
+
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.error(f"Unexpected error: {str(e)}")
+        raise HTTPException(status_code=500, detail="Internal server error")
+
+@app.post("/feedback", response_model=FeedbackResponse)
+async def feedback(feedback: FeedbackRequest, file: UploadFile = File(...)):
+    if not file.filename.endswith(('.mp4', '.avi', '.mov')):
+        raise HTTPException(status_code=400, detail="Invalid file format. Only .mp4, .avi, .mov are supported.")
+
+    try:
+        violations = validate_feedback_actions(feedback.actions, action_pairs, ACTION_COLS)
+        if violations:
+            raise ValueError(f"Invalid feedback actions: {'; '.join(violations)}")
+
+        video_bytes = await file.read()
+        save_feedback(feedback.timestamp, feedback.actions, feedback.probabilities, video_bytes)
+
+        return FeedbackResponse(
+            status="success",
+            message="Feedback submitted successfully"
+        )
+
+    except ValueError as ve:
+        raise HTTPException(status_code=400, detail=str(ve))
+    except Exception as e:
+        logger.error(f"Unexpected error in feedback: {str(e)}")
+        raise HTTPException(status_code=500, detail="Internal server error")
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+@app.post("/retrain")
+async def retrain():
+    try:
+        if not os.path.exists(CONFIG['feedback_csv']):
+            raise HTTPException(status_code=404, detail="No feedback data available for retraining")
+        
+        retrain_model(model, CONFIG['feedback_csv'], "train.csv", device, num_epochs=1)
+        return {"status": "success", "message": "Model retraining completed"}
+    
+    except HTTPException as he:
+        raise he
+    except ValueError as ve:
+        raise HTTPException(status_code=400, detail=str(ve))
+    except Exception as e:
+        logger.error(f"Unexpected error in retraining: {str(e)}")
+        raise HTTPException(status_code=500, detail="Internal server error")

backend/models/model_metadata.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:124fb9d4226d36d5abbd666615bc08000d4518bdf523b8e70c5c87b4972bd381
+size 874

backend/models/timesformer_best_submission.csv

@@ -0,0 +1,281 @@
+id,Filename,Time,Advancing,Retreating,Enclosing,Spreading,Rising,Descending,Directing,Indirecting,Increasing Pressure,Decreasing Pressure,Acceleration,Decelerating
+cdd7e8f4-825f-4e6a-a0ba-a12a4ecf8676,s06_smiley,t0440,0,1,1,0,0,0,1,1,1,0,1,1
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backend/preprocessed_cache/81a5fb6b1b9354ad5073dd5fca4d07f8_0732.pkl

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+size 4817621

backend/preprocessed_cache/81a5fb6b1b9354ad5073dd5fca4d07f8_0753.pkl

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+size 4817621

my-app/README.md

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+# My App
+
+This project integrates Gradio for webcam video recording and Streamlit for action tracking. It allows users to record video clips from their webcam and manage actions with timestamps.
+
+## Project Structure
+
+```
+my-app
+├── app.py
+├── requirements.txt
+└── README.md
+```
+
+## Installation
+
+To set up the project, you need to install the required packages. Run the following command in your terminal:
+
+```
+pip install -r requirements.txt
+```
+
+## Running the Application
+
+To run the application, execute the following command:
+
+```
+python app.py
+```
+
+## Features
+
+- Live webcam feed with 5-second rolling clip uploads.
+- Action tracking with timestamps.
+- User-friendly interface for managing actions.
+
+## Dependencies
+
+This project requires the following Python packages:
+
+- Gradio
+- OpenCV
+- Streamlit
+
+Make sure to have these installed before running the application.

my-app/requirements.txt

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+gradio
+opencv-python
+streamlit
+pandas

requirement.txt

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+streamlit
+opencv-python
+requests
+numpy