Update app.py

app.py

@@ -1,75 +1,59 @@
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import JSONResponse
-from models.model_wav2vec import Wav2VecIntent
-from huggingface_hub import hf_hub_download
-import torch
-import soundfile as sf
-import gradio as gr
-import numpy as np
-import librosa
-
-app = FastAPI()
-
-# Download model from Hugging Face
-MODEL_PATH = hf_hub_download(repo_id="avi292423/speech-intent-recognition-project", filename="wav2vec_best_model.pt")
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-label_map = {
-    "activate_lamp": 0, "activate_lights": 1, "activate_lights_bedroom": 2, "activate_lights_kitchen": 3,
-    "activate_lights_washroom": 4, "activate_music": 5, "bring_juice": 6, "bring_newspaper": 7,
-    "bring_shoes": 8, "bring_socks": 9, "change_language_Chinese": 10, "change_language_English": 11,
-    "change_language_German": 12, "change_language_Korean": 13, "change_language_none": 14,
-    "deactivate_lamp": 15, "deactivate_lights": 16, "deactivate_lights_bedroom": 17, "deactivate_lights_kitchen": 18,
-    "deactivate_lights_washroom": 19, "deactivate_music": 20, "decrease_heat": 21, "decrease_heat_bedroom": 22,
-    "decrease_heat_kitchen": 23, "decrease_heat_washroom": 24, "decrease_volume": 25, "increase_heat": 26,
-    "increase_heat_bedroom": 27, "increase_heat_kitchen": 28, "increase_heat_washroom": 29, "increase_volume": 30
-}
-index_to_label = {v: k for k, v in label_map.items()}
-
-num_classes = 31
-pretrained_model = "facebook/wav2vec2-base"  # Use base for less RAM, or keep large if needed
-model = Wav2VecIntent(num_classes=num_classes, pretrained_model=pretrained_model).to(device)
-state_dict = torch.load(MODEL_PATH, map_location=device)
-model.load_state_dict(state_dict)
-model.eval()
-
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)):
-    audio_bytes = await file.read()
-    with open("temp.wav", "wb") as f:
-        f.write(audio_bytes)
-    audio, sample_rate = sf.read("temp.wav")
-    if sample_rate != 16000:
-        return JSONResponse({"error": "Audio must have a sample rate of 16kHz."}, status_code=400)
-    waveform = torch.tensor(audio, dtype=torch.float32).unsqueeze(0).to(device)
-    with torch.no_grad():
-        output = model(waveform)
-        predicted_class = torch.argmax(output, dim=1).item()
-        predicted_label = index_to_label.get(predicted_class, "Unknown Class")
-    return {"prediction": predicted_label}
-
-def predict_intent(audio):
-    if audio is None:
-        return "No audio provided."
-    sr, y = audio
-    if sr != 16000:
-        # Resample to 16kHz
-        y = librosa.resample(y.astype(float), orig_sr=sr, target_sr=16000)
-        sr = 16000
-    waveform = torch.tensor(y, dtype=torch.float32).unsqueeze(0).to(device)
-    with torch.no_grad():
-        output = model(waveform)
-        predicted_class = torch.argmax(output, dim=1).item()
-        predicted_label = index_to_label.get(predicted_class, "Unknown Class")
-    return predicted_label
-
-demo = gr.Interface(
-    fn=predict_intent,
-    inputs=gr.Audio(source="microphone", type="numpy", label="Record or Upload Audio (16kHz WAV)"),
-    outputs=gr.Textbox(label="Predicted Intent"),
-    title="Speech Intent Recognition",
-    description="Record or upload a 16kHz WAV audio file to predict the intent."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from models.model_wav2vec import Wav2VecIntent
+from huggingface_hub import hf_hub_download
+import torch
+import soundfile as sf
+import numpy as np
+import librosa
+
+app = FastAPI()
+
+# Enable CORS for all origins (so your frontend can call the API)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Download model from Hugging Face
+MODEL_PATH = hf_hub_download(repo_id="avi292423/speech-intent-recognition-project", filename="wav2vec_best_model.pt")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+label_map = {
+    "activate_lamp": 0, "activate_lights": 1, "activate_lights_bedroom": 2, "activate_lights_kitchen": 3,
+    "activate_lights_washroom": 4, "activate_music": 5, "bring_juice": 6, "bring_newspaper": 7,
+    "bring_shoes": 8, "bring_socks": 9, "change_language_Chinese": 10, "change_language_English": 11,
+    "change_language_German": 12, "change_language_Korean": 13, "change_language_none": 14,
+    "deactivate_lamp": 15, "deactivate_lights": 16, "deactivate_lights_bedroom": 17, "deactivate_lights_kitchen": 18,
+    "deactivate_lights_washroom": 19, "deactivate_music": 20, "decrease_heat": 21, "decrease_heat_bedroom": 22,
+    "decrease_heat_kitchen": 23, "decrease_heat_washroom": 24, "decrease_volume": 25, "increase_heat": 26,
+    "increase_heat_bedroom": 27, "increase_heat_kitchen": 28, "increase_heat_washroom": 29, "increase_volume": 30
+}
+index_to_label = {v: k for k, v in label_map.items()}
+
+num_classes = 31
+pretrained_model = "facebook/wav2vec2-large"  # Use large model
+model = Wav2VecIntent(num_classes=num_classes, pretrained_model=pretrained_model).to(device)
+state_dict = torch.load(MODEL_PATH, map_location=device)
+model.load_state_dict(state_dict)
+model.eval()
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    audio_bytes = await file.read()
+    with open("temp.wav", "wb") as f:
+        f.write(audio_bytes)
+    audio, sample_rate = sf.read("temp.wav")
+    if sample_rate != 16000:
+        # Resample to 16kHz
+        audio = librosa.resample(audio.astype(float), orig_sr=sample_rate, target_sr=16000)
+    waveform = torch.tensor(audio, dtype=torch.float32).unsqueeze(0).to(device)
+    with torch.no_grad():
+        output = model(waveform)
+        predicted_class = torch.argmax(output, dim=1).item()
+        predicted_label = index_to_label.get(predicted_class, "Unknown Class")
+    return {"prediction": predicted_label}