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README.md

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 ---
 license: apache-2.0
+tags:
+  - tensorflow-lite
+  - edge-ai
+  - asl-recognition
+  - mediapipe
+  - computer-vision
+  - gesture-recognition
+library_name: tensorflow
+inference: false
+datasets: []
+model-index:
+  - name: ASL-TFLite-Edge
+    results: []
 ---
+
+# ASL-TFLite-Edge
+
+This repository contains a TensorFlow Lite model trained to recognize American Sign Language (ASL) fingerspelling gestures using hand landmark data. The model is optimized for real-time inference on edge devices.
+
+## 🧠 Model Details
+
+- **Format:** TensorFlow Lite (.tflite)
+- **Input:** 64x64 RGB image (generated from hand landmarks via Mediapipe)
+- **Output:** Softmax probabilities over 59 ASL character classes (including a padding token)
+- **Frameworks:** TensorFlow, Mediapipe
+
+## 📁 Files Included
+
+- `asl_model.tflite` – The TFLite model file for ASL recognition
+- `inference_args.json` – JSON file containing the selected columns used for inference from parquet data
+- `tflite_inference.py` – Inference script to run predictions from raw `.parquet` landmark files
+
+## 🚀 How to Run Inference
+
+### Requirements
+```bash
+pip install -r requirements.txt
+```
+
+### Run the Script
+```bash
+python tflite_inference.py path/to/sample.parquet
+```
+
+### Output
+```bash
+Predicted class index: 5
+```
+>🔁 You can map this class index back to the character using your `char_to_num` mapping used during training.
+
+## 📌 Example Workflow
+1. Extract right-hand landmark data from Mediapipe and store it in a `.parquet` file.
+
+2. Ensure it contains the same selected_columns as in `inference_args.json`.
+
+3. Run `tflite_inference.py` to get the predicted class.
+
+## 🧾 License
+This project is licensed under the Apache 2.0 License.
+
+## 👨‍💻 Author
+Developed by Manik Sheokand
+
+For sign language fingerspelling Recognition on edge devices using TensorFlow Lite

requirements.txt

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+tensorflow>=2.9.0
+mediapipe>=0.10.0
+pandas
+scikit-image
+pyarrow
+matplotlib

tflite_inference.py

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+import numpy as np
+import pandas as pd
+import json
+import tensorflow as tf
+import mediapipe as mp
+from skimage.transform import resize
+import matplotlib.pyplot as plt
+from mediapipe.framework.formats import landmark_pb2
+from PIL import Image
+
+# Load selected columns for inference
+with open("inference_args.json", "r") as f:
+    SEL_COLS = json.load(f)["selected_columns"]
+
+# Load TFLite model
+interpreter = tf.lite.Interpreter(model_path="asl_model.tflite")
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Drawing utilities
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_hands = mp.solutions.hands
+
+def load_relevant_data_subset(pq_path):
+    return pd.read_parquet(pq_path, columns=SEL_COLS)
+
+def draw_hand_landmarks(seq_df):
+    images = []
+    for seq_idx in range(len(seq_df)):
+        x_hand = seq_df.iloc[seq_idx].filter(regex="x_right_hand.*").values
+        y_hand = seq_df.iloc[seq_idx].filter(regex="y_right_hand.*").values
+        z_hand = seq_df.iloc[seq_idx].filter(regex="z_right_hand.*").values
+
+        right_hand_image = np.zeros((600, 600, 3))
+        right_hand_landmarks = landmark_pb2.NormalizedLandmarkList()
+
+        for x, y, z in zip(x_hand, y_hand, z_hand):
+            right_hand_landmarks.landmark.add(x=x, y=y, z=z)
+
+        mp_drawing.draw_landmarks(
+            right_hand_image,
+            right_hand_landmarks,
+            mp_hands.HAND_CONNECTIONS,
+            landmark_drawing_spec=mp_drawing_styles.get_default_hand_landmarks_style()
+        )
+        images.append(right_hand_image)
+    return images
+
+def preprocess_image(image):
+    img = resize(image, (64, 64), preserve_range=True).astype(np.float32) / 255.0
+    return np.expand_dims(img, axis=0)
+
+def predict_from_parquet(parquet_path):
+    df = load_relevant_data_subset(parquet_path)
+    image_seq = draw_hand_landmarks(df)
+    if not image_seq:
+        raise ValueError("No hand image generated.")
+    img = preprocess_image(image_seq[len(image_seq) // 2])
+    interpreter.set_tensor(input_details[0]['index'], img)
+    interpreter.invoke()
+    output = interpreter.get_tensor(output_details[0]['index'])
+    prediction = np.argmax(output)
+    return prediction
+
+if __name__ == "__main__":
+    import sys
+    if len(sys.argv) < 2:
+        print("Usage: python tflite_inference.py <parquet_file_path>")
+    else:
+        parquet_file = sys.argv[1]
+        pred = predict_from_parquet(parquet_file)
+        print("Predicted class index:", pred)