Update app.py

app.py

@@ -1,93 +1,24 @@
 import gradio as gr
-import cv2
-import numpy as np
-import tempfile
-import os
+from model.predictor import LBWPredictor
+from utils.preprocess import clean_input
 
-# === CONFIG ===
-FRAME_SKIP = 3
-MAX_FRAMES = 200
-RESIZE_DIM = (640, 360)
+predictor = LBWPredictor("model/lbw_model.joblib")
 
-def track_ball_in_frame(frame):
-    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
-    
-    # 🎯 Color thresholds for red ball
-    lower_red1 = np.array([0, 100, 100])
-    upper_red1 = np.array([10, 255, 255])
-    lower_red2 = np.array([160, 100, 100])
-    upper_red2 = np.array([180, 255, 255])
+def predict_interface(**kwargs):
+    features = clean_input(kwargs)
+    return predictor.predict(features)
 
-    mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
-    mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
-    mask = mask1 | mask2
-    mask = cv2.GaussianBlur(mask, (5, 5), 0)
-
-    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    
-    if contours:
-        largest = max(contours, key=cv2.contourArea)
-        if cv2.contourArea(largest) > 20:
-            (x, y), radius = cv2.minEnclosingCircle(largest)
-            center = (int(x), int(y))
-            radius = int(radius)
-            cv2.circle(frame, center, radius, (0, 255, 0), 2)
-            cv2.putText(frame, "Ball", (center[0]+10, center[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-    
-    return frame
-
-def process_frame(frame, i):
-    return track_ball_in_frame(frame)
-
-def analyze_video(video_path):
-    cap = cv2.VideoCapture(video_path)
-    frames = []
-    frame_idx = 0
-    processed_count = 0
-    
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        frame_idx += 1
-        if frame_idx % FRAME_SKIP != 0:
-            continue
-
-        frame = cv2.resize(frame, RESIZE_DIM)
-        processed_frame = process_frame(frame, frame_idx)
-
-        frames.append(processed_frame)
-        processed_count += 1
-
-        if processed_count >= MAX_FRAMES:
-            break
-
-    cap.release()
-
-    output_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
-    height, width, _ = frames[0].shape
-    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), 15, (width, height))
-
-    for f in frames:
-        out.write(f)
-    out.release()
-
-    return output_path, f"✔️ Processed {processed_count} frames from {frame_idx} total."
-
-# === Gradio UI ===
 with gr.Blocks() as demo:
-    gr.Markdown("## 🏏 GullyDRS - LBW Predictor with Ball Tracking")
-    gr.Markdown("Upload a short cricket delivery video (.mp4) to detect and track the ball!")
-
-    with gr.Row():
-        video_input = gr.File(file_types=[".mp4"], type="filepath", label="Upload Cricket Delivery (.mp4)")
-        submit_btn = gr.Button("Track Ball")
-
-    with gr.Row():
-        video_output = gr.Video(label="Tracked Ball Output (.mp4)")
-        status_output = gr.Textbox(label="Processing Status")
-
-    submit_btn.click(fn=analyze_video, inputs=video_input, outputs=[video_output, status_output])
+    gr.Markdown("# GullyDRS: LBW Predictor 🏏")
+    inputs = [
+        gr.Number(label="Ball Speed (km/h)", value=130),
+        gr.Number(label="Impact X", value=0.25),
+        gr.Number(label="Impact Y", value=0.5),
+        gr.Number(label="Stump Height", value=0.71)
+    ]
+    btn = gr.Button("Predict LBW")
+    output = gr.Textbox(label="Decision")
+
+    btn.click(predict_interface, inputs=inputs, outputs=output)
 
 demo.launch()