Create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+import whisper
+import os
+
+# Load a smaller model for faster build on Spaces (change to "large-v3" if you have resources)
+model = whisper.load_model("base")
+
+def format_time(seconds):
+    h = int(seconds // 3600)
+    m = int((seconds % 3600) // 60)
+    s = int(seconds % 60)
+    ms = int((seconds - int(seconds)) * 1000)
+    return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"
+
+def generate_srt(video_path):
+    # video_path is path to uploaded file on server
+    result = model.transcribe(video_path, language="en", task="transcribe")
+    segments = result.get("segments", [])
+
+    srt_lines = []
+    for i, seg in enumerate(segments, start=1):
+        start = seg["start"]
+        end = seg["end"]
+        text = seg["text"].strip()
+        srt_lines.append(str(i))
+        srt_lines.append(f"{format_time(start)} --> {format_time(end)}")
+        srt_lines.append(text)
+        srt_lines.append("")  # blank line
+
+    srt_text = "\n".join(srt_lines)
+    srt_path = os.path.join(os.getcwd(), "subtitles.srt")
+    with open(srt_path, "w", encoding="utf-8") as f:
+        f.write(srt_text)
+
+    return srt_path
+
+def process(video):
+    # Gradio passes a file path for uploaded file
+    if isinstance(video, dict):
+        # older gradio versions sometimes pass dict with "name"
+        video_path = video.get("name") or video.get("filename") or list(video.values())[0]
+    else:
+        video_path = video
+    return generate_srt(video_path)
+
+demo = gr.Interface(
+    fn=process,
+    inputs=gr.Video(source="upload", label="Upload your video"),
+    outputs=gr.File(label="Download SRT (.srt)"),
+    title="BollySubs — Indian English Subtitle Generator",
+    description="Upload a video and get SRT subtitles optimized for Indian English accents."
+)
+
+if __name__ == "__main__":
+    demo.launch()