Update app.py

app.py

@@ -1,20 +1,57 @@
-
 import streamlit as st
 from transformers import pipeline
 import google.generativeai as genai
 import json
 import random
 
+# Language dictionary for interface translations
+LANGUAGES = {
+    'English': {
+        'title': 'Emotionally Aware Chatbot',
+        'input_placeholder': 'Enter your message:',
+        'emotions_header': 'Detected Emotions:',
+        'response_header': 'AI Response:'
+    },
+    'Arabic': {
+        'title': 'روبوت الدردشة العاطفي',
+        'input_placeholder': 'أدخل رسالتك:',
+        'emotions_header': 'العواطف المكتشفة:',
+        'response_header': 'استجابة الذكاء الاصطناعي:'
+    },
+    'French': {
+        'title': 'Chatbot Émotionnel',
+        'input_placeholder': 'Entrez votre message:',
+        'emotions_header': 'Émotions Détectées:',
+        'response_header': 'Réponse IA:'
+    },
+    'Spanish': {
+        'title': 'Chatbot Emocional',
+        'input_placeholder': 'Introduzca su mensaje:',
+        'emotions_header': 'Emociones Detectadas:',
+        'response_header': 'Respuesta de IA:'
+    },
+    'Dutch': {
+        'title': 'Emotioneel Chatbot',
+        'input_placeholder': 'Voer uw bericht in:',
+        'emotions_header': 'Gedetecteerde Emoties:',
+        'response_header': 'AI-Antwoord:'
+    },
+    'Turkish': {
+        'title': 'Duygusal Sohbet Robotu',
+        'input_placeholder': 'Mesajınızı girin:',
+        'emotions_header': 'Algılanan Duygular:',
+        'response_header': 'Yapay Zeka Yanıtı:'
+    }
+}
+
 # Load the JSON data
 with open('emotion_templates.json', 'r') as f:
     data = json.load(f)
     
 # Configure Gemini (replace with your API key)
 genai.configure(api_key="AIzaSyCYRYNwCU1f9cgJYn8pd86Xcf6hiSMwJr0")
-
 model = genai.GenerativeModel('gemini-2.0-flash') 
 
-
 def generate_text(prompt, context=""):
     """
     Generates text using the Gemini model.
@@ -26,75 +63,82 @@ def generate_text(prompt, context=""):
         print(f"Error generating text: {e}")
         return "I am sorry, I encountered an error while generating the text."
 
-def create_prompt(emotion, topic = None):
-  """
-  Chooses a random prompt from the template list.
-  """
-
-  templates = data["emotion_templates"][emotion]
-  prompt = random.choice(templates)
-
-  if topic:
-    prompt = prompt.replace("[topic/person]",topic)
-    prompt = prompt.replace("[topic]",topic)
-    prompt = prompt.replace("[person]",topic)
-    prompt = prompt.replace("[object]",topic) # added object replace
-    prompt = prompt.replace("[outcome]",topic) # added outcome replace
-  subfix_prompt = "Make the generated text in the same language as the topic.\n"
-  subfix_prompt =subfix_prompt+ "Make the generated text short.\n"
-  
-  prefix_prompt = "## topic language and contant\n"+topic
-  prompt = prefix_prompt + prompt + subfix_prompt
-  return prompt
-
+def create_prompt(emotion, topic=None):
+    """
+    Chooses a random prompt from the template list.
+    """
+    templates = data["emotion_templates"][emotion]
+    prompt = random.choice(templates)
+    if topic:
+        # Replace various placeholders in the prompt
+        placeholders = ["[topic/person]", "[topic]", "[person]", "[object]", "[outcome]"]
+        for placeholder in placeholders:
+            prompt = prompt.replace(placeholder, topic)
+    
+    subfix_prompt = "Make the generated text in the same language as the topic.\n"
+    subfix_prompt += "Make the generated text short.\n"
+    
+    prefix_prompt = "## topic language and content\n" + topic
+    prompt = prefix_prompt + prompt + subfix_prompt
+    return prompt
     
-
 # 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="AnasAlokla/multilingual_go_emotions")  # Or choose another model
+emotion_classifier = pipeline("text-classification", model="AnasAlokla/multilingual_go_emotions")
 
 # 2. Conversational Agent Logic
 def get_ai_response(user_input, emotion_predictions):
     """Generates AI response based on user input and detected emotions."""
-
-
     dominant_emotion = None
     max_score = 0
     responses = None
     for prediction in emotion_predictions:
-      if prediction['score'] > max_score:
-        max_score = prediction['score']
-        dominant_emotion = prediction['label']
-
-
-    prompt_text = create_prompt(dominant_emotion,user_input)
-
+        if prediction['score'] > max_score:
+            max_score = prediction['score']
+            dominant_emotion = prediction['label']
+    
+    prompt_text = create_prompt(dominant_emotion, user_input)
     responses = generate_text(prompt_text)
     
     # Handle cases where no specific emotion is clear
     if dominant_emotion is None:
-       return "Error for response"
+        return "Error for response"
     else:
         return responses
 
 # 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)
+def main():
+    # Language Selection
+    selected_language = st.sidebar.selectbox(
+        "Select Interface Language",
+        list(LANGUAGES.keys()),
+        index=0  # Default to English
+    )
+    
+    # Set page title and header based on selected language
+    st.title(LANGUAGES[selected_language]['title'])
+    
+    # Input Text Box
+    user_input = st.text_input(
+        LANGUAGES[selected_language]['input_placeholder'], 
+        ""
+    )
+    
+    if user_input:
+        # Emotion Detection
+        emotion_predictions = emotion_classifier(user_input)
+        
+        # Display Emotions
+        st.subheader(LANGUAGES[selected_language]['emotions_header'])
+        for prediction in emotion_predictions:
+            st.write(f"- {prediction['label']}: {prediction['score']:.2f}")
+        
+        # Get AI Response
+        ai_response = get_ai_response(user_input, emotion_predictions)
+        
+        # Display AI Response
+        st.subheader(LANGUAGES[selected_language]['response_header'])
+        st.write(ai_response)
+
+# Run the main function
+if __name__ == "__main__":
+    main()