Delete app.py

app.py

@@ -1,133 +0,0 @@
-
-import gradio as gr
-from transformers import pipeline
-import whisper
-from collections import Counter
-import matplotlib.pyplot as plt
-
-# Load models
-emotion_classifier = pipeline("audio-classification", model="superb/hubert-large-superb-er")
-whisper_model = whisper.load_model("base")
-
-# Chart generator
-def create_emotion_chart(labels, scores):
-    emoji_map = {
-        "hap": "😊 Happy", "sad": "😔 Sad", "neu": "😐 Neutral",
-        "ang": "😠 Angry", "fea": "😨 Fear", "dis": "🤢 Disgust", "sur": "😮 Surprise"
-    }
-    color_map = {
-        "hap": "#facc15", "sad": "#60a5fa", "neu": "#a1a1aa",
-        "ang": "#ef4444", "fea": "#818cf8", "dis": "#14b8a6", "sur": "#f472b6"
-    }
-    labels = [emoji_map.get(label, label) for label in labels]
-    colors = [color_map.get(label, "#60a5fa") for label in labels]
-
-    fig, ax = plt.subplots(figsize=(5, 3.5))
-    bars = ax.barh(labels, scores, color=colors, edgecolor="black", height=0.5)
-    for bar, score in zip(bars, scores):
-        ax.text(bar.get_width() + 0.02, bar.get_y() + bar.get_height() / 2,
-                f"{score:.2f}", va='center', fontsize=10, color='black')
-    ax.set_xlim(0, 1)
-    ax.set_title("🎭 Emotion Confidence Scores", fontsize=13, pad=10)
-    ax.invert_yaxis()
-    ax.set_facecolor("#f9fafb")
-    fig.patch.set_facecolor("#f9fafb")
-    for spine in ax.spines.values():
-        spine.set_visible(False)
-    ax.tick_params(axis='x', colors='gray')
-    ax.tick_params(axis='y', colors='gray')
-    return fig
-
-def generate_next_moves(dominant_emotion, conf_score, transcript=""):
-    suggestions = []
-    harsh_words = ["bad", "ugly", "terrible", "hate", "worst"]
-    positive_tone_negative_words = any(word in transcript.lower() for word in harsh_words) if "happiness" in dominant_emotion else False
-
-    if 'sadness' in dominant_emotion:
-        suggestions.append("Your tone feels low — try lifting the pitch slightly to bring more warmth.")
-        suggestions.append("Even if the words are positive, a brighter tone helps convey enthusiasm.")
-    elif 'happiness' in dominant_emotion and conf_score >= 80:
-        suggestions.append("Nice energy! Try modulating your tone even more for emphasis in key moments.")
-        suggestions.append("Experiment with subtle emotional shifts as you speak for more depth.")
-    elif 'neutral' in dominant_emotion:
-        suggestions.append("Add inflection to break a monotone pattern — especially at the ends of sentences.")
-        suggestions.append("Highlight your message by stressing emotionally important words.")
-    elif conf_score < 50:
-        suggestions.append("Try exaggerating vocal ups and downs when reading to unlock more expression.")
-        suggestions.append("Slow down slightly and stretch certain words to vary your delivery.")
-    else:
-        suggestions.append("Keep practicing tone variation — you’re building a solid base.")
-
-    if positive_tone_negative_words:
-        suggestions.append("Your tone was upbeat, but the word choices were harsh — aim to align both for better impact.")
-    return "\n- " + "\n- ".join(suggestions)
-
-def generate_personacoach_report(emotions, transcript):
-    report = "## 📝 Your PersonaCoach Report\n---\n\n"
-    report += "### 🗒️ What You Said:\n"
-    report += f"> _{transcript.strip()}_\n\n"
-
-    label_map = {
-        'hap': '😊 happiness', 'sad': '😔 sadness', 'neu': '😐 neutral',
-        'ang': '😠 anger', 'fea': '😨 fear', 'dis': '🤢 disgust', 'sur': '😮 surprise'
-    }
-    for e in emotions:
-        e['emotion'] = label_map.get(e['label'], e['label'])
-
-    scores = [e['score'] for e in emotions]
-    top_score = max(scores)
-    conf_score = int(top_score * 100)
-
-    emotion_labels = [e['emotion'] for e in emotions if e['score'] >= 0.2]
-    dominant_emotion = emotion_labels[0] if emotion_labels else "neutral"
-
-    report += f"### 🎯 Tone Strength:\n- Your tone scored **{conf_score}/100** in clarity.\n\n"
-    report += "### 🗣️ Emotion & Delivery:\n"
-    if emotion_labels:
-        emo_str = ", ".join([f"{e['emotion']} ({e['score']:.2f})" for e in emotions])
-        report += f"- Emotionally, your voice showed: {emo_str}\n"
-    else:
-        report += "- Your tone wasn’t clearly expressive. Try reading with a bit more emphasis or emotion.\n"
-
-    filler_words = ["um", "uh", "like", "you know", "so", "actually", "basically", "literally"]
-    words = transcript.lower().split()
-    total_words = len(words)
-    filler_count = sum(words.count(fw) for fw in filler_words)
-    filler_ratio = filler_count / total_words if total_words > 0 else 0
-
-    report += "\n### 💬 Pausing Style:\n"
-    report += f"- {filler_count} fillers out of {total_words} words.\n"
-    if filler_ratio > 0.06:
-        report += "- Try pausing instead of fillers.\n"
-    elif filler_ratio > 0.03:
-        report += "- A few fillers — consider tightening up delivery.\n"
-    else:
-        report += "- Strong fluency — great control.\n"
-
-    report += "\n### 🧭 Next Moves:\n"
-    report += generate_next_moves(dominant_emotion, conf_score, transcript)
-    return report
-
-def analyze_audio(audio_path):
-    result = whisper_model.transcribe(audio_path)
-    transcript = result['text']
-    emotion_results = emotion_classifier(audio_path)
-    labels = [r['label'] for r in emotion_results]
-    scores = [r['score'] for r in emotion_results]
-    fig = create_emotion_chart(labels, scores)
-    report = generate_personacoach_report(emotion_results, transcript)
-    return transcript, fig, report
-
-interface = gr.Interface(
-    fn=analyze_audio,
-    inputs=gr.Audio(type="filepath", label="Upload your voice (.wav only)"),
-    outputs=[
-        gr.Textbox(label="📝 Transcription"),
-        gr.Plot(label="🎭 Emotion Chart"),
-        gr.Markdown(label="📄 PersonaCoach Feedback")
-    ],
-    title="SPEAK – Speech Performance Evaluation and Affective Knowledge",
-    description="Upload a voice sample and get coaching feedback on tone, emotion, and fluency."
-)
-
-interface.launch()