Upload app(1).py

app(1).py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1i-Mo3pDk4cm6BpSRL37pXCvCqWNBlO7X
+"""
+
+from __future__ import annotations
+
+import gradio as gr
+import whisper
+from transformers import pipeline
+from gradio.themes.base import Base
+from gradio.themes.utils import colors, fonts, sizes
+from typing import Iterable
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+import matplotlib
+matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+
+
+model = whisper.load_model("base")
+sentiment_analysis = pipeline("sentiment-analysis", framework="pt", model="SamLowe/roberta-base-go_emotions")
+
+def analyze_sentiment(text):
+    results = sentiment_analysis(text)
+    sentiment_results = {result['label']: result['score'] for result in results}
+    return sentiment_results
+
+def get_sentiment_emoji(sentiment):
+    # Define the emojis corresponding to each sentiment
+    emoji_mapping = {
+        "disappointment": "😞",
+        "sadness": "😢",
+        "annoyance": "😠",
+        "neutral": "😐",
+        "disapproval": "👎",
+        "realization": "😮",
+        "nervousness": "😬",
+        "approval": "👍",
+        "joy": "😄",
+        "anger": "😡",
+        "embarrassment": "😳",
+        "caring": "🤗",
+        "remorse": "😔",
+        "disgust": "🤢",
+        "grief": "😥",
+        "confusion": "😕",
+        "relief": "😌",
+        "desire": "😍",
+        "admiration": "😌",
+        "optimism": "😊",
+        "fear": "😨",
+        "love": "❤️",
+        "excitement": "🎉",
+        "curiosity": "🤔",
+        "amusement": "😄",
+        "surprise": "😲",
+        "gratitude": "🙏",
+        "pride": "🦁"
+    }
+    return emoji_mapping.get(sentiment, "")
+
+def display_sentiment_results(sentiment_results, option):
+    sentiment_text = ""
+    for sentiment, score in sentiment_results.items():
+        emoji = get_sentiment_emoji(sentiment)
+        if option == "Sentiment Only":
+            sentiment_text += f"{sentiment} {emoji}\n"
+        elif option == "Sentiment + Score":
+            sentiment_text += f"{sentiment} {emoji}: {score}\n"
+    return sentiment_text
+
+def inference(audio, sentiment_option):
+    audio = whisper.load_audio(audio)
+    audio = whisper.pad_or_trim(audio)
+
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+    _, probs = model.detect_language(mel)
+    lang = max(probs, key=probs.get)
+
+    options = whisper.DecodingOptions(fp16=False)
+    result = whisper.decode(model, mel, options)
+
+    sentiment_results = analyze_sentiment(result.text)
+    sentiment_output = display_sentiment_results(sentiment_results, sentiment_option)
+
+    return lang.upper(), result.text, sentiment_output
+
+title = """<h1 align="center">Audio Sentiment Analysis</h1>"""
+subtitle = """<h6 align="center">Automatic Speech Recognition</h6>"""
+image_path = "/Users/rayespinoza/PycharmProjects/AnalyticsProjects/Styles/Arquitecture.jpg"
+description = """
+<p align="justify">With cross-modal interaction and AI (tools and pre-trained models in NLP), we can analyze large audio data
+in real-time, such as recorded conversations, customer service calls, or voice recordings, in order to identify and categorize
+emotions (from positive and neutral to sad and angry.</p><br>
+
+Components of the tool:<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Input: Real-time multilingual<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Video Call speech recognition<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Pre-trained model: Whisper<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Model size: Large with 769M Parameters<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Encoder/Decoder Arquitecture <br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Transcribe, Translate, and Identify Audio<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Output: Sentiment analysis<br>
+<br>
+"""
+
+custom_css = """
+banner-image {
+    margin-left: auto;
+    margin-right: auto;
+}
+chat-message {
+    font-size: 300px;
+    min-height: 600px;
+}
+
+img {
+  border-radius: 8px;
+  max-width: 100%;
+  height: auto;
+}
+
+"""
+
+#-----Themes config:
+
+class Seafoam(Base):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.emerald,
+        secondary_hue: colors.Color | str = colors.blue,
+        neutral_hue: colors.Color | str = colors.blue,
+        spacing_size: sizes.Size | str = sizes.spacing_md,
+        radius_size: sizes.Size | str = sizes.radius_md,
+        text_size: sizes.Size | str = sizes.text_lg,
+        font: fonts.Font
+        | str
+        | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("Quicksand"),
+            "ui-sans-serif",
+            "sans-serif",
+        ),
+        font_mono: fonts.Font
+        | str
+        | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("IBM Plex Mono"),
+            "ui-monospace",
+            "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            spacing_size=spacing_size,
+            radius_size=radius_size,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        super().set(
+            body_background_fill="repeating-linear-gradient(45deg, *primary_200, *primary_200 10px, *primary_50 10px, *primary_50 20px)",
+            body_background_fill_dark="repeating-linear-gradient(45deg, *primary_800, *primary_800 10px, *primary_900 10px, *primary_900 20px)",
+            button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
+            button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)",
+            button_primary_text_color="white",
+            button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)",
+            slider_color="*secondary_300",
+            slider_color_dark="*secondary_600",
+            block_title_text_weight="600",
+            block_border_width="3px",
+            block_shadow="*shadow_drop_lg",
+            button_shadow="*shadow_drop_lg",
+            button_large_padding="32px",
+        )
+
+
+seafoam = Seafoam()
+#
+
+lock_symbol = '\U0001F512' # 🔒
+unlock_symbol = '\U0001F513' # 🔓
+switch_values_symbol = '\U000021C5' # ⇅
+
+class FormRow(gr.Row, gr.components.FormComponent):
+    """Same as gr.Row but fits inside gradio forms"""
+
+    def get_block_name(self):
+        return "row"
+
+class ToolButton(gr.Button, gr.components.FormComponent):
+    """Small button with single emoji as text, fits inside gradio forms"""
+
+    def __init__(self, **kwargs):
+        super().__init__(variant="tool", **kwargs)
+
+    def get_block_name(self):
+        return "button"
+
+def toggle_aspect_ratio(btn):
+    if btn == unlock_symbol:
+        return gr.update(value = lock_symbol, variant="primary")
+    else:
+        return gr.update(value = unlock_symbol, variant="secondary")
+
+
+#
+
+with open('styles.css', 'r') as f:
+    css_app = f.read()
+
+
+block = gr.Blocks(css=custom_css, theme='gradio/default',title="Analytics Projects by Ray Espinoza")
+#block = gr.Blocks(css=custom_css, title="Analytics Projects by Ray Espinoza")
+#block = gr.Blocks(css=".gradio-container {background-color: black}", title="Analytics Projects by Ray Espinoza")
+#block = gr.Blocks(css=".gradio-container {background: url('file=pic4.jpg')}", title="Analytics Projects by Ray Espinoza")
+
+with block:
+    gr.HTML(title)
+    gr.HTML(subtitle)
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Image(image_path, elem_id="banner-image", show_label=False, show_download_button=False)
+            #banner-image
+            #gr.Markdown(value=image_path, elem_id="img")
+            #gr.Image(image_path, elem_id="chat-message", show_label=False)
+        with gr.Column():
+            gr.HTML(description)
+
+    with gr.Group():
+        with gr.Box():
+            audio = gr.Audio(
+                label="Input Audio",
+                show_label=False,#Here#False
+                source="microphone",
+                type="filepath"
+            )
+
+            sentiment_option = gr.Radio(
+                choices=["Sentiment Only", "Sentiment + Score"],
+                label="Select an option",
+                default="Sentiment Only"
+            )
+
+            btn = gr.Button("Execute: Transcribe",variant="primary")
+
+        lang_str = gr.Textbox(label="Language:")
+
+        text = gr.Textbox(label="Transcription:")
+
+        sentiment_output = gr.Textbox(label="Sentiment Analysis Results:", output=True)
+
+        btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output])
+
+        gr.HTML('''
+        <div class="footer">
+            <p>By <a href="https://github.com" style="text-decoration: underline;" target="_blank"> Ray EH Github</a>
+            </p>
+        </div>
+        ''')
+
+block.launch()