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Update app.py
Browse files
app.py
CHANGED
@@ -40,7 +40,7 @@ suggestion_database = {
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}
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}
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# Function to fetch relevant resources
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def get_relevant_resources(emotion):
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resources = suggestion_database.get(emotion, {})
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return resources.get("suggestions", []), resources.get("articles", []), resources.get("videos", [])
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@@ -63,7 +63,7 @@ def predict_emotion_single(response, emotion_analyzer):
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if emotion_analyzer is None:
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st.error("Model not loaded. Please try reloading the app.")
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return {"Error": "Emotion analyzer model not initialized. Please check model loading."}
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-
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try:
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result = emotion_analyzer([response])
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return {res["label"]: round(res["score"], 4) for res in result}
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@@ -72,12 +72,8 @@ def predict_emotion_single(response, emotion_analyzer):
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return {"Error": f"Prediction failed: {e}"}
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# Streamlit App Layout
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st.title("
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st.write("**How it works:**")
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st.write("- Enter your thoughts or feelings.")
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st.write("- Our AI analyzes your text to predict your emotional state.")
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st.write("- Receive personalized suggestions to improve your well-being.")
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# Define questions for the user
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questions = [
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@@ -113,23 +109,23 @@ for i, question in enumerate(questions, start=1):
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responses[question] = (user_response, analysis)
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st.write(f"**Your Response**: {user_response}")
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st.write(f"**Emotion Analysis**: {analysis}")
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# Based on the emotion, suggest activities, articles, and videos
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max_emotion = max(analysis, key=analysis.get) if analysis else "neutral"
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suggestions, articles, videos = get_relevant_resources(max_emotion)
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if suggestions:
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st.write(f"### π§ Suggested Activity: {suggestions[0]}")
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else:
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st.write("### π§ No suggestions available at the moment.")
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if articles:
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st.write(f"### π Suggested Articles:")
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for article in articles:
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st.write(f"[{article['title']}]({article['url']})")
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else:
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st.write("### π No articles available at the moment.")
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if videos:
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st.write(f"### π₯ Suggested Videos:")
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for video in videos:
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}
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}
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# Function to fetch relevant resources (placeholder - needs actual API calls or database)
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def get_relevant_resources(emotion):
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resources = suggestion_database.get(emotion, {})
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return resources.get("suggestions", []), resources.get("articles", []), resources.get("videos", [])
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if emotion_analyzer is None:
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st.error("Model not loaded. Please try reloading the app.")
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return {"Error": "Emotion analyzer model not initialized. Please check model loading."}
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try:
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result = emotion_analyzer([response])
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return {res["label"]: round(res["score"], 4) for res in result}
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return {"Error": f"Prediction failed: {e}"}
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# Streamlit App Layout
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st.title("Behavior Prediction App")
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st.write("Enter your thoughts or feelings, and let the app predict your emotional states.")
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# Define questions for the user
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questions = [
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responses[question] = (user_response, analysis)
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st.write(f"**Your Response**: {user_response}")
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st.write(f"**Emotion Analysis**: {analysis}")
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# Based on the emotion, suggest activities, articles, and videos
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max_emotion = max(analysis, key=analysis.get) if analysis else "neutral"
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suggestions, articles, videos = get_relevant_resources(max_emotion)
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if suggestions:
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st.write(f"### π§ Suggested Activity: {suggestions[0]}")
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else:
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st.write("### π§ No suggestions available at the moment.")
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if articles:
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st.write(f"### π Suggested Articles:")
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for article in articles:
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st.write(f"[{article['title']}]({article['url']})")
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else:
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st.write("### π No articles available at the moment.")
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if videos:
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st.write(f"### π₯ Suggested Videos:")
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for video in videos:
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