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app (1).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 = emotion_prediction + transcribed_text
+    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"),
+    outputs=[
+        gr.Label("Acoustic Prediction", label="Acoustic Prediction"),
+        gr.Label("Transcribed Text", label="Transcribed Text"),
+        gr.Image(type='pil', label="Generated Image")
+    ],
+    title="Affective Virtual Environments",
+    description="Create an AVE using your voice."
+)
+
+
+interface.launch()

mymodel_SER_LSTM_RAVDESS (1).h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ad725c49cec0f25f17e1c798f35d3b3e486ffdf2cf97497f2beb99805dc6c8f
+size 976728

requirements.txt

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+kaleido
+numpy
+tensorflow==2.12.0
+gradio
+transformers
+tf-keras
+librosa
+vaderSentiment
+requests
+torch
+sentencepiece
+faster_whisper