Create app.py

app.py

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+
+
+import streamlit as st
+from transformers import pipeline
+
+# 1. Emotion Detection Model (Using Hugging Face's transformer)
+# Choose a suitable model -  'emotion-classification' is the task, you can specify a model from Hugging Face Model Hub.
+emotion_classifier = pipeline("text-classification", model="SamLowe/roberta-base-go_emotions")  # Or choose another model
+
+# 2. Conversational Agent Logic
+def get_ai_response(user_input, emotion_predictions):
+    """Generates AI response based on user input and detected emotions."""
+
+    # Basic response generation based on detected emotions
+    responses = {
+        "anger": "I understand you're feeling angry.  Let's take a deep breath and try to resolve this.",
+        "sadness": "I'm sorry to hear you're feeling sad. Is there anything I can do to help?",
+        "joy": "That's wonderful! I'm so happy for you!",
+        "surprise": "Wow, that's surprising! Tell me more.",
+        "fear": "I understand you're afraid. How can I help?",
+        "neutral": "Understood.", # or a more neutral response
+        "default": "I am not able to understand the emotion, please try again"
+    }
+
+
+    dominant_emotion = None
+    max_score = 0
+
+    for prediction in emotion_predictions:
+      if prediction['score'] > max_score:
+        max_score = prediction['score']
+        dominant_emotion = prediction['label']
+
+
+    # Handle cases where no specific emotion is clear
+    if dominant_emotion is None:
+       return responses["default"]  # or use default message if no emotion is detected.
+    elif dominant_emotion in responses:
+        return responses[dominant_emotion]
+    else:
+        return "I'm detecting some emotion, but I'm not sure how to respond." #Handle unexpected emotion labels.
+
+# 3. Streamlit Frontend
+st.title("Emotionally Aware Chatbot")
+
+# Input Text Box
+user_input = st.text_input("Enter your message:", "")
+
+if user_input:
+    # Emotion Detection
+    emotion_predictions = emotion_classifier(user_input)
+
+    # Display Emotions
+    st.subheader("Detected Emotions:")
+    for prediction in emotion_predictions:
+        st.write(f"- {prediction['label']}: {prediction['score']:.2f}") # Show emotion score.
+
+    # Get AI Response
+    ai_response = get_ai_response(user_input, emotion_predictions)
+
+    # Display AI Response
+    st.subheader("AI Response:")
+    st.write(ai_response)