Update app.py

app.py

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+import gradio as gr
+import numpy as np
+import librosa
+import requests
+from io import BytesIO
+from PIL import Image
+import os
+from tensorflow.keras.models import load_model
+from faster_whisper import WhisperModel
+
+# Load the emotion prediction model
+def load_emotion_model(model_path):
+    try:
+        model = load_model(model_path)
+        return model
+    except Exception as e:
+        print("Error loading emotion prediction model:", e)
+        return None
+
+model_path = 'mymodel_SER_LSTM_RAVDESS.h5'
+model = load_emotion_model(model_path)
+
+# Initialize WhisperModel
+model_size = "small"
+model2 = WhisperModel(model_size, device="cpu", compute_type="int8")
+
+# Function to transcribe audio
+def transcribe(wav_filepath):
+    segments, _ = model2.transcribe(wav_filepath, beam_size=5)
+    return "".join([segment.text for segment in segments])
+
+# Function to extract MFCC features from audio
+def extract_mfcc(wav_file_name):
+    try:
+        y, sr = librosa.load(wav_file_name)
+        mfccs = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40).T, axis=0)
+        return mfccs
+    except Exception as e:
+        print("Error extracting MFCC features:", e)
+        return None
+
+# Emotions dictionary
+emotions = {1: 'neutral', 2: 'calm', 3: 'happy', 4: 'sad', 5: 'angry', 6: 'fearful', 7: 'disgust', 8: 'surprised'}
+
+# Function to predict emotion from audio
+def predict_emotion_from_audio(wav_filepath):
+    try:
+        test_point = extract_mfcc(wav_filepath)
+        if test_point is not None:
+            test_point = np.reshape(test_point, newshape=(1, 40, 1))
+            predictions = model.predict(test_point)
+            predicted_emotion_label = np.argmax(predictions[0]) + 1
+            return emotions[predicted_emotion_label]
+        else:
+            return "Error: Unable to extract features"
+    except Exception as e:
+        print("Error predicting emotion:", e)
+        return None
+
+api_key = os.getenv("DeepAI_api_key")
+
+# Function to generate an image using DeepAI Text to Image API
+def generate_image(api_key, text):
+    url = "https://api.deepai.org/api/text2img"
+    headers = {'api-key': api_key}
+    response = requests.post(
+        url,
+        data={'text': text},
+        headers=headers
+    )
+    response_data = response.json()
+    if 'output_url' in response_data:
+        image_url = response_data['output_url']
+        image_response = requests.get(image_url)
+        image = Image.open(BytesIO(image_response.content))
+        return image
+    else:
+        return None
+
+# Function to get predictions
+def get_predictions(audio_input):
+    emotion_prediction = predict_emotion_from_audio(audio_input)
+    transcribed_text = transcribe(audio_input)
+    texto_imagen = "Generate Patagonian Monsters' with a "+ emotion_prediction + " attitude, representing the idea of: [" + transcribed_text + "]. Illustrate this using asemic writings in an old map style." 
+    image = generate_image(api_key, texto_imagen)
+    return emotion_prediction, transcribed_text, image
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=get_predictions,
+    inputs=gr.Audio(label="Input Audio", type="filepath", sources=["microphone"]),     # gr.Audio(label="Input Audio", type="filepath")
+    outputs=[
+        gr.Label("Acoustic Prediction", label="Acoustic Prediction", visible=False),
+        gr.Label("Transcribed Text", label="Transcribed Text", visible=False),
+        gr.Image(type='pil', label="Generated Image")
+    ],
+    title="Terra Australis Ignota",
+    description="Terra Australis Ignota is a Multimedia project.\n\n The term refers to a common cartographic reference used during the 15th to 18th centuries, which alluded to territories about which little was known, such as Patagonia, or Antarctica, among other places. These territories were commonly attributed with fantastical and monstrous characteristics. The nature of the South America was depicted as exotic, wild, other, and, in this sense, monstrous.\n\n Record your voice with a sentence imagining/remembering Patagonia."
+)
+
+interface.launch()