final project

app.py

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+import gradio as gr
+import whisper
+from transformers import pipeline
+
+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">🎤 Sentiment analysis for Voice Calls 💬</h1>"""
+image_path = "genai.png"
+description = """ This POC was developed for AI FINTECH HACKATHON @ BHARATPE"""
+
+custom_css = """
+#banner-image {
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+}
+#chat-message {
+    font-size: 14px;
+    min-height: 300px;
+}
+"""
+
+block = gr.Blocks(css=custom_css)
+
+with block:
+    gr.HTML(title)
+
+    with gr.Row():
+        with gr.Column():
+            gr.Image(image_path, elem_id="banner-image", show_label=False)
+        with gr.Column():
+            gr.HTML(description)
+
+    with gr.Group():
+        with gr.Group():
+            audio = gr.Audio(
+                label="Input Audio",
+                show_label=False,
+                type="filepath"
+            )
+
+            sentiment_option = gr.Radio(
+                choices=["Sentiment Only", "Sentiment + Score"],
+                label="Select an option",
+                # default="Sentiment Only"
+            )
+
+            btn = gr.Button("Transcribe")
+
+        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])
+
+       
+
+block.launch()

requirements.txt

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+git+https://github.com/openai/whisper.git 
+transformers
+gradio
+torch
+torchaudio
+torchvision