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Update app.py
Browse files
app.py
CHANGED
@@ -54,7 +54,45 @@ SUPPORTED_LANGUAGES = {
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'tr': 'Turkish'
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}
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def detect_language(text):
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"""Detect the language of the input text."""
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@@ -165,54 +203,69 @@ def load_emotion_classifier(model_name):
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try:
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# Use the HF token if available for authentication
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if hf_token:
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return pipeline("text-classification", model=model_name, use_auth_token=hf_token,top_k=None)
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else:
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return pipeline("text-classification", model=model_name,top_k=None)
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except Exception as e:
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st.error(f"Error loading model {model_name}: {str(e)}")
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return None
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def get_ai_response(user_input, emotion_predictions, detected_language):
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"""Generates AI response based on user input, detected emotions, and language."""
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def display_top_predictions(emotion_predictions, selected_language, num_predictions=3):
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"""Display top emotion predictions in sidebar."""
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for i, prediction in enumerate(top_predictions, 1):
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emotion = prediction['label']
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score = prediction['score']
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percentage = score * 100
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#
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st.sidebar.markdown(f"**{i}. {emotion.title()}**")
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st.sidebar.progress(score)
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st.sidebar.markdown(f"Score: {percentage:.1f}%")
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st.sidebar.markdown("---")
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def display_language_info(detected_language, confidence_scores=None):
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"""Display detected language information."""
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@@ -258,17 +311,7 @@ def main():
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# Language detection settings
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#st.sidebar.subheader("π Language Detection")
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#auto_detect = st.sidebar.checkbox("Auto-detect input language", value=True)
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auto_detect=True
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#if not auto_detect:
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# manual_language = st.sidebar.selectbox(
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# "Select input language manually:",
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# list(SUPPORTED_LANGUAGES.keys()),
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# format_func=lambda x: SUPPORTED_LANGUAGES[x],
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# index=0
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# )
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# Load the selected emotion classifier
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emotion_classifier = load_emotion_classifier(selected_model_key)
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@@ -301,76 +344,78 @@ def main():
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)
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if user_input:
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# detected_language = manual_language
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# Display language detection results
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display_language_info(detected_language)
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# Emotion Detection
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with st.spinner("Analyzing emotions..."):
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emotion_predictions = emotion_classifier(user_input)
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# Display top predictions in sidebar
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display_top_predictions(emotion_predictions, selected_language, num_predictions)
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# Display Emotions in main area (top 5)
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st.subheader(LANGUAGES[selected_language]['emotions_header'])
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top_5_emotions = sorted(emotion_predictions, key=lambda x: x['score'], reverse=True)[:5]
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# Create columns for better display
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col1, col2 = st.columns(2)
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for i, prediction in enumerate(top_5_emotions):
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emotion = prediction['label']
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score = prediction['score']
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percentage = score * 100
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#
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category_emoji = "π" if emotion_category == "positive" else "π" if emotion_category == "negative" else "π"
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# Run the main function
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if __name__ == "__main__":
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'tr': 'Turkish'
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}
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def normalize_emotion_predictions(predictions):
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"""
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Normalize emotion predictions to ensure consistent format.
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Handles different return formats from Hugging Face pipelines.
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"""
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try:
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# If predictions is a list of lists (multiple inputs)
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if isinstance(predictions, list) and len(predictions) > 0:
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if isinstance(predictions[0], list):
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# Take the first prediction set
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predictions = predictions[0]
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# Ensure each prediction has the required keys
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normalized = []
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for pred in predictions:
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if isinstance(pred, dict):
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# Handle different possible key names
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label = pred.get('label') or pred.get('LABEL') or pred.get('emotion', 'unknown')
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score = pred.get('score') or pred.get('SCORE') or pred.get('confidence', 0.0)
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normalized.append({
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'label': str(label).lower(),
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'score': float(score)
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})
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else:
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# Handle unexpected format
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st.warning(f"Unexpected prediction format: {pred}")
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continue
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return normalized if normalized else [{'label': 'neutral', 'score': 1.0}]
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else:
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# Handle case where predictions is not in expected format
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st.warning(f"Unexpected predictions format: {type(predictions)}")
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return [{'label': 'neutral', 'score': 1.0}]
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except Exception as e:
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st.error(f"Error normalizing predictions: {str(e)}")
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return [{'label': 'neutral', 'score': 1.0}]
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def detect_language(text):
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"""Detect the language of the input text."""
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try:
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# Use the HF token if available for authentication
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if hf_token:
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return pipeline("text-classification", model=model_name, use_auth_token=hf_token, top_k=None)
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else:
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return pipeline("text-classification", model=model_name, top_k=None)
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except Exception as e:
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st.error(f"Error loading model {model_name}: {str(e)}")
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return None
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def get_ai_response(user_input, emotion_predictions, detected_language):
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"""Generates AI response based on user input, detected emotions, and language."""
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try:
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# Ensure predictions are normalized
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normalized_predictions = normalize_emotion_predictions(emotion_predictions)
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dominant_emotion = None
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max_score = 0
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for prediction in normalized_predictions:
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if prediction['score'] > max_score:
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max_score = prediction['score']
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dominant_emotion = prediction['label']
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if dominant_emotion is None:
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return "Error: No emotion detected for response generation."
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# Create enhanced prompt with language and emotion context
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prompt_text = create_enhanced_prompt(dominant_emotion, user_input, detected_language, max_score)
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response = generate_text(prompt_text)
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return response
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except Exception as e:
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st.error(f"Error generating AI response: {str(e)}")
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return "I'm sorry, I encountered an error while generating a response."
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def display_top_predictions(emotion_predictions, selected_language, num_predictions=3):
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"""Display top emotion predictions in sidebar."""
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# Normalize predictions first
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normalized_predictions = normalize_emotion_predictions(emotion_predictions)
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# Sort predictions by score in descending order
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sorted_predictions = sorted(normalized_predictions, key=lambda x: x['score'], reverse=True)
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# Take top N predictions
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top_predictions = sorted_predictions[:num_predictions]
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# Display in sidebar
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st.sidebar.markdown("---")
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st.sidebar.subheader("π― Top Emotion Predictions")
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for i, prediction in enumerate(top_predictions, 1):
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emotion = prediction['label']
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score = prediction['score']
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percentage = score * 100
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# Create a progress bar for visual representation
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st.sidebar.markdown(f"**{i}. {emotion.title()}**")
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st.sidebar.progress(score)
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st.sidebar.markdown(f"Score: {percentage:.1f}%")
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st.sidebar.markdown("---")
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except Exception as e:
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st.sidebar.error(f"Error displaying predictions: {str(e)}")
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def display_language_info(detected_language, confidence_scores=None):
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"""Display detected language information."""
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# Language detection settings
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auto_detect = True
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# Load the selected emotion classifier
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emotion_classifier = load_emotion_classifier(selected_model_key)
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)
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if user_input:
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try:
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# Language Detection
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if auto_detect:
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detected_language = detect_language(user_input)
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# Display language detection results
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display_language_info(detected_language)
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# Emotion Detection
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with st.spinner("Analyzing emotions..."):
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emotion_predictions = emotion_classifier(user_input)
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# Normalize predictions
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normalized_predictions = normalize_emotion_predictions(emotion_predictions)
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# Display top predictions in sidebar
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display_top_predictions(emotion_predictions, selected_language, num_predictions)
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# Display Emotions in main area (top 5)
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st.subheader(LANGUAGES[selected_language]['emotions_header'])
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top_5_emotions = sorted(normalized_predictions, key=lambda x: x['score'], reverse=True)[:5]
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# Create columns for better display
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col1, col2 = st.columns(2)
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for i, prediction in enumerate(top_5_emotions):
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emotion = prediction['label']
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score = prediction['score']
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percentage = score * 100
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# Add emotion category indicator
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emotion_category = categorize_emotion(emotion)
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category_emoji = "π" if emotion_category == "positive" else "π" if emotion_category == "negative" else "π"
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if i % 2 == 0:
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with col1:
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st.metric(
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label=f"{category_emoji} {emotion.title()}",
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value=f"{percentage:.1f}%",
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delta=None
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)
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else:
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with col2:
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st.metric(
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label=f"{category_emoji} {emotion.title()}",
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value=f"{percentage:.1f}%",
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delta=None
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)
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# Get AI Response with enhanced emotional intelligence
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with st.spinner("Generating emotionally intelligent response..."):
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ai_response = get_ai_response(user_input, emotion_predictions, detected_language)
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# Display AI Response
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st.subheader(f"π€ {LANGUAGES[selected_language]['response_header']}")
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# Show dominant emotion and response language
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dominant_emotion = max(normalized_predictions, key=lambda x: x['score'])
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language_name = get_language_name(detected_language)
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# Display the response in a nice container
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with st.container():
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st.write(ai_response)
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# Add emotion intensity indicator
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emotion_score = dominant_emotion['score']
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intensity = "High" if emotion_score > 0.7 else "Moderate" if emotion_score > 0.4 else "Low"
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st.caption(f"Emotion Intensity: {intensity} ({emotion_score:.2f})")
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except Exception as e:
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st.error(f"An error occurred: {str(e)}")
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st.error("Please try again with different input or check your configuration.")
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# Run the main function
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if __name__ == "__main__":
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