Create app.py

app.py

@@ -0,0 +1,137 @@
+import gradio as gr
+from datasets import load_dataset
+import subprocess
+import os
+import tempfile
+
+
+def convert_ts_to_mp4(dataset_name, file_name):
+    """
+    Downloads a .ts video file from a Hugging Face dataset,
+    converts it to .mp4 using ffmpeg, and returns the path
+    to the .mp4 file.
+
+    Args:
+        dataset_name (str): The name of the Hugging Face dataset.
+        file_name (str): The name of the .ts file within the dataset.
+                        It should be just the filename, not the full path.
+
+    Returns:
+        str: The path to the converted .mp4 file, or None on error.
+    """
+    try:
+        # 1. Load the dataset
+        dataset = load_dataset(dataset_name, streaming=True)
+
+        # 2. Find the file.  This part assumes the filename is unique
+        #    within the dataset.  For more complex datasets, you might
+        #    need a more sophisticated search (e.g., iterating through
+        #    splits and checking file metadata).  This also assumes
+        #    that the dataset provides the files in a way that we can
+        #    access them directly.
+        file_url = None
+        for split in dataset.keys():  # Iterate through the splits
+            for example in dataset[split]:
+                if "file" in example and os.path.basename(example["file"]) == file_name:
+                    file_url = example["file"]
+                    break
+                elif isinstance(example, dict):  # Check for nested file paths.
+                    for key, value in example.items():
+                        if isinstance(value, str) and os.path.basename(value) == file_name:
+                            file_url = value;
+                            break
+            if file_url:
+                break
+
+        if not file_url:
+            return "Error: File not found in the dataset."
+
+        # 3. Download the .ts file to a temporary location
+        with tempfile.NamedTemporaryFile(suffix=".ts", delete=True) as ts_file:
+            # Use a simple download mechanism.  For more robust
+            # downloading, especially with large files, consider
+            # using 'requests' with streaming.
+            try:
+                import urllib.request
+                urllib.request.urlretrieve(file_url, ts_file.name)
+            except Exception as e:
+                return f"Error downloading file: {e}"
+
+            # 4. Convert the .ts file to .mp4 using ffmpeg in a temporary location
+            with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as mp4_file:
+                try:
+                    subprocess.run(
+                        [
+                            "ffmpeg",
+                            "-i",
+                            ts_file.name,
+                            "-c:v",
+                            "libx264",  # Use libx264 for H.264 encoding (common)
+                            "-c:a",
+                            "aac",  # Use AAC for audio encoding (common)
+                            "-y",  # Overwrite output file if it exists
+                            mp4_file.name,
+                        ],
+                        check=True,  # Raise an exception on non-zero exit code
+                        stdout=subprocess.PIPE,
+                        stderr=subprocess.PIPE,
+                    )
+                except subprocess.CalledProcessError as e:
+                    # ffmpeg failed.  Return the error message.
+                    error_message = f"FFmpeg conversion failed: {e.stderr.decode('utf-8')}"
+                    print(error_message)  # Print to console for debugging in Spaces
+                    return error_message
+
+                # 5. Return the path to the .mp4 file
+                return mp4_file.name
+
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+
+def gradio_interface():
+    """
+    Defines the Gradio interface for the application.
+    """
+    inputs = [
+        gr.Textbox(
+            label="Hugging Face Dataset Name",
+            placeholder="e.g., 'PolyAI/minds-14'",
+        ),
+        gr.Textbox(
+            label="TS File Name (within the dataset)",
+            placeholder="e.g., 'file_name.ts'",
+        ),
+    ]
+    outputs = gr.File(label="Converted MP4 File")  # Use gr.File for downloadable files
+
+    title = "TS to MP4 Converter"
+    description = (
+        "Convert .ts video files from Hugging Face datasets to .mp4 format. "
+        "Provide the dataset name and the name of the .ts file.  The converted "
+        ".mp4 file will be available for download."
+    )
+
+    # Example Usage (Corrected)
+    article = """
+    Example Usage:
+
+    1.  For the 'PolyAI/minds-14' dataset and the file 'audio/en/common_voice_en_7722.ts', 
+        enter 'PolyAI/minds-14' in the \"Hugging Face Dataset Name\" field and 
+        'common_voice_en_7722.ts' in the \"TS File Name\" field (note: the example dataset in the original prompt did not contain .ts files, so I've provided a placeholder.  You'll need to adapt this to a dataset that actually *does* have .ts files).
+    2.  Click the 'Submit' button.
+    3.  The converted .mp4 file will be processed, and a download link will be provided.
+    """
+
+    return gr.Interface(
+        fn=convert_ts_to_mp4,
+        inputs=inputs,
+        outputs=outputs,
+        title=title,
+        description=description,
+        article=article,
+    )
+
+
+if __name__ == "__main__":
+    gradio_interface().launch()