Upload folder using huggingface_hub

README.md

@@ -1,13 +1,14 @@
 ---
-title: Host Model Gra
-emoji: 🏢
-colorFrom: gray
-colorTo: yellow
+title: host_model_gra
+emoji: 🚀
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.22.0
-python_version: '3.12'
+sdk_version: 5.0.0
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# host_model_gra
+
+Minimal Gradio app scaffold, ready to deploy to Hugging Face Spaces.

app.py

@@ -0,0 +1,64 @@
+import os
+
+import gradio as gr
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+MODEL_ID = os.environ.get("HF_ASR_MODEL", "masumtechnonext/wav2vec2-arabic-letter-verifier")
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+processor = Wav2Vec2Processor.from_pretrained(MODEL_ID, token=HF_TOKEN)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID, token=HF_TOKEN)
+model.eval()
+
+
+def transcribe(audio):
+    if audio is None:
+        return "", "Record or upload audio first."
+
+    sample_rate, waveform = audio
+    waveform = torch.tensor(waveform, dtype=torch.float32)
+    if waveform.ndim > 1:
+        waveform = waveform.mean(dim=-1)
+
+    inputs = processor(
+        waveform.numpy(),
+        sampling_rate=sample_rate,
+        return_tensors="pt",
+        padding=True,
+    )
+
+    with torch.no_grad():
+        logits = model(inputs.input_values).logits
+
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)[0].strip()
+    return transcription
+
+
+def verify(audio, expected_letter):
+    transcription = transcribe(audio)
+    if not expected_letter:
+        return transcription, "Enter an expected letter to verify."
+
+    is_match = transcription.strip() == expected_letter.strip()
+    verdict = "✅ Match" if is_match else "❌ No match"
+    return transcription, verdict
+
+
+demo = gr.Interface(
+    fn=verify,
+    inputs=[
+        gr.Audio(sources=["microphone", "upload"], type="numpy", label="Speak the letter"),
+        gr.Textbox(label="Expected letter (optional)"),
+    ],
+    outputs=[
+        gr.Textbox(label="Transcription"),
+        gr.Textbox(label="Verification"),
+    ],
+    title="Arabic Letter Verifier",
+    description=f"Model: {MODEL_ID}",
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+torch
+transformers