Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from collections import Counter
+
+# Load model
+model_name = "prithivMLmods/Alphabet-Sign-Language-Detection"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = SiglipForImageClassification.from_pretrained(model_name)
+
+def predict_from_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    predictions = []
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Convert frame to RGB
+        img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # Run model
+        inputs = processor(images=img, return_tensors="pt")
+        outputs = model(**inputs)
+        probs = outputs.logits.softmax(dim=-1)[0]
+
+        idx = int(probs.argmax())
+        label = model.config.id2label[idx]
+        predictions.append(label)
+
+    cap.release()
+
+    # Majority vote
+    if predictions:
+        most_common = Counter(predictions).most_common(1)[0]
+        return f"Predicted Letter: {most_common[0]} (appeared {most_common[1]} times)"
+    else:
+        return "No frames processed."
+
+iface = gr.Interface(
+    fn=predict_from_video,
+    inputs=gr.Video(),   # ✅ no 'type' argument in Gradio 5.x
+    outputs="text",
+    title="ASL Alphabet Recognition from Video",
+    description="Upload a short video of your ASL sign (A–Z). The system will analyze frames and predict the most likely letter."
+)
+
+iface.launch()