Update app.py

app.py

@@ -318,6 +318,42 @@ rag_prompt = PromptTemplate.from_template(template)
 
 retriever = vectorstore.as_retriever()
 
+import requests
+
+API_TOKEN = TOKEN
+
+model_name = "facebook/nllb-200-distilled-600M"
+
+url = f"https://api-inference.huggingface.co/models/{model_name}"
+
+headers = {
+    "Authorization": f"Bearer {API_TOKEN}"
+}
+
+def translate_text(text, src_lang, tgt_lang):
+    """Translate text using Hugging Face API"""
+    response = requests.post(
+        url,
+        headers=headers,
+        json={
+            "inputs": text,
+            "parameters": {
+                "src_lang": src_lang,
+                "tgt_lang": tgt_lang
+            }
+        }
+    )
+
+    if response.status_code == 200:
+        result = response.json()
+        if isinstance(result, list) and len(result) > 0:
+            return result[0]['translation_text']
+        return result['translation_text']
+    else:
+        print(f"Translation error: {response.status_code}, {response.text}")
+        return text  # Return original text if translation fails
+
+
 class OpenRouterLLM:
     def __init__(self, key: str):
         try:
@@ -356,7 +392,7 @@ class UserSession:
         self.welcome_message = None
         self.conversation_history = []  # Add conversation history storage
         self.llm = llm  # Store the LLM instance
-    
+
     def set_user(self, user_info):
         self.current_user = user_info
         self.set_welcome_message(user_info.get("Nickname", "Guest"))
@@ -365,13 +401,12 @@ class UserSession:
         self.conversation_history = [
             {"role": "assistant", "content": welcome},
         ]
-    
+
     def get_user(self):
         return self.current_user
-    
-    def set_welcome_message(self, Nickname):
+
+    def set_welcome_message(self, Nickname, src_lang="eng_Latn", tgt_lang="kin_Latn"):
         """Set a dynamic welcome message using the OpenRouterLLM."""
-        # Define a prompt for the LLM to generate a welcome message
         prompt = (
             f"Create a very brief welcome message for {Nickname} that fits in 3 lines. "
             f"The message should: "
@@ -381,28 +416,31 @@ class UserSession:
             f"4. Use a tone that is warm, reassuring, and professional. "
             f"5. Keep the message concise and impactful, ensuring it fits within the character limit."
         )
-        
+
         # Use the OpenRouterLLM to generate the message
-        welcome_text = "".join(self.llm.stream(prompt))  # Stream and concatenate the response
-        
+        welcome = "".join(self.llm.stream(prompt))  # Stream and concatenate the response
+        welcome_text=translate_text(welcome, src_lang, tgt_lang)
+
         # Format the message with HTML styling
         self.welcome_message = (
+            f"<div style='font-size: 24px; font-weight: bold; color: #2E86C1;'>"
+            f"Welcome {Nickname}! 👋</div>"
             f"<div style='font-size: 20px;'>"
             f"{welcome_text}"
             f"</div>"
         )
-    
+
     def get_welcome_message(self):
         return self.welcome_message
-    
+
     def add_to_history(self, role, message):
         """Add a message to the conversation history"""
         self.conversation_history.append({"role": role, "content": message})
-    
+
     def get_conversation_history(self):
         """Get the full conversation history"""
         return self.conversation_history
-    
+
     def get_formatted_history(self):
         """Get conversation history formatted as a string for the LLM"""
         formatted_history = ""
@@ -411,11 +449,12 @@ class UserSession:
             formatted_history += f"{role}: {entry['content']}\n\n"
         return formatted_history
 
-api_key =api  
-llm_instance = OpenRouterLLM(key=api_key)  
+api_key =api 
+llm_instance = OpenRouterLLM(key=api_key)
+#llm_instance = model
 user_session = UserSession(llm_instance)
 
-# Store user details and handle session
+
 def collect_user_info(Nickname):
     if not Nickname:
         return "Nickname is required to proceed.", gr.update(visible=False), gr.update(visible=True), []
@@ -474,68 +513,53 @@ def create_rag_chain(retriever, template, api_key):
 
     return stream_func
 
-def rag_memory_stream(message, history):
-    # Add user message to history
-    user_session.add_to_history("user", message)
+# def rag_memory_stream(message, history):
+#     # Add user message to history
+#     user_session.add_to_history("user", message)
     
-    # Initialize with empty response
-    partial_text = ""
-    full_response = ""
-
-    # Use the rag_chain with the question
-    for new_text in rag_chain({"question": message}):
-        partial_text += new_text
-        full_response = partial_text
-        yield partial_text
+#     # Initialize with empty response
+#     partial_text = ""
+#     full_response = ""
+
+#     # Use the rag_chain with the question
+#     for new_text in rag_chain({"question": message}):
+#         partial_text += new_text
+#         full_response = partial_text
+#         yield partial_text
     
-    # After generating the complete response, add it to history
-    user_session.add_to_history("assistant", full_response)
+#     # After generating the complete response, add it to history
+#     user_session.add_to_history("assistant", full_response)
 
 
-import gradio as gr
-api_key = api
-# Gradio Interface Setup with improved UX
-def chatbot_interface():
-    # Get API key (in a real application, handle this more securely)
-    api_key = api  # This should be properly defined or imported elsewhere
+def rag_memory_stream(message, history, user_lang="kin_Latn", system_lang="eng_Latn"):
+    english_message = translate_text(message, user_lang, system_lang)
 
-    # Update the template to include conversation history
-    # template = """
-    global template
-    # template = """
-    # You are a compassionate and supportive AI assistant specializing in helping individuals affected by Gender-Based Violence (GBV) with emotional awareness.
+    user_session.add_to_history("user", english_message)
 
-    #   **Previous conversation:**
-    #   {conversation_history}
+    full_response = ""
 
-    #   **Context information:**
-    #   {context}
+    for new_text in rag_chain({"question": english_message}):
+        full_response += new_text
 
-    #   **User's Question:** {question}
 
-    #   Respond to {first_name} with these guidelines:
+    translated_response = translate_text(full_response, system_lang, user_lang)
+
+    user_session.add_to_history("assistant", full_response)
+    
+    yield translated_response
+
+
+
+import gradio as gr
 
-    #   1. **Emotional Awareness**
-    #     - Acknowledge emotions with empathy (e.g., "I understand how you feel")
-    #     - Offer comfort for negative emotions
 
-    #   2. **Communication Approach**
-    #     - Use gentle check-ins when appropriate (e.g., "How are you holding up today, {first_name}?")
-    #     - Occasionaly use a warm welcome or check-in (e.g., "Hello {first_name}! How are you feeling today?")
-    #     - Use occasional appropriate emojis (😊, 🤗, ❤️)
-    #     - Keep responses concise unless details are requested
-    #     - Maintain warm but professional tone
-    #     - Refer to {conversation_history} to maintain conversation
+api_key = api
 
-    #   3. **Information Handling**
-    #     - Extract relevant details from the {context}
-    #     - Provide links when specifically requested
-    #     - If information isn't available, respond with: "I don't have that information at the moment, {first_name}. 😊"
-    #     - Never generate unsupported content or speculate
+def chatbot_interface():
+    api_key = api
+ 
+    global template
 
-    #   Your response should be supportive, accurate, and centered on the user's immediate needs.
-    # """
-    # Global template for GBV support chatbot
     template = """
     You are a compassionate and supportive AI assistant specializing in helping individuals affected by Gender-Based Violence (GBV). Your primary goal is to provide emotionally intelligent support while maintaining appropriate boundaries.
     
@@ -582,10 +606,7 @@ def chatbot_interface():
     Your response should balance emotional support with practical guidance, always centered on {first_name}'s expressed needs and current emotional state.
     """
 
-    # # Create the RAG chain with user context
-    # rag_chain = create_rag_chain(retriever, template, api_key)
-    
-    # Create the RAG chain with user context
+
     global rag_chain
     rag_chain = create_rag_chain(retriever, template, api_key)