Update app.py

app.py

@@ -9,8 +9,7 @@ embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-
 
 import pandas as pd
 
-# Set folder path
-folder_path = "/content/drive/MyDrive/Ijwi_folder"
+folder_path = "./"
 context_data = []
 
 # List all files in the folder
@@ -52,9 +51,7 @@ def extract_text_from_pdf(pdf_path):
         print(f"Error extracting text from {pdf_path}: {e}")
         return ""
 
-# Folder containing the PDFs
-folder_path ="/content/drive/MyDrive/Ijwi_folder"  # Update with your actual folder path
-
+folder_path = "./"
 # Initialize the list to hold the extracted text chunks
 text_chunks = []
 
@@ -257,9 +254,11 @@ vectorstore.get().keys()
 vectorstore.add_texts(data)
 
 
+api= os.environ.get('V1')
 
 
 
+ 
 
 from openai import OpenAI
 from langchain_core.prompts import PromptTemplate
@@ -269,115 +268,292 @@ import gradio as gr
 from typing import Iterator
 import time
 
-
-# Template with user personalization and improved welcome message
+# Refined Template with Emotional Awareness
 template = ("""
-    You are a friendly and intelligent chatbot designed to assist users in a conversational and human-like manner. Your goal is to provide accurate, helpful, and engaging responses based on the provided context: {context}. Follow these guidelines:
-
-    1. **Contextual Interaction**
-      - Begin with a warm and empathetic welcome message
-      - Extract precise details from provided context: {context}
-      - Respond directly to user's question: {question}
-      - Remember the user's name is {first_name} and address them by name occasionally not always
-
-    2. **Communication Guidelines**
-      - Maintain warm, conversational tone
-      - Use occasional emojis for engagement
-      - Provide clear, concise information
-
-    3. **Response Strategies**
-      - Greet users naturally and ask about their wellbeing (e.g., "Hello {first_name}! 😊 How are you feeling today?", "Welcome, {first_name}! 😊 You're in a safe and caring space. What's on your mind today?")
-      - Always start with a check-in about the user's wellbeing or current situation
-      - Deliver only relevant information
-      - Avoid generating content beyond context
-      - Handle missing information transparently
-
-    4. **No Extra Content**
-      - If no information matches user's request:
+    You are a friendly and empathetic chatbot designed to assist users in a conversational and human-like manner. Your goal is to provide accurate, helpful, and emotionally supportive responses based on the provided context: {context}. Follow these guidelines:
+
+    1. **Emotional Awareness**
+      - Acknowledge the user's emotions and respond with empathy.
+      - Use phrases like "I understand how you feel," "That sounds challenging," or "I'm here to support you."
+      - If the user expresses negative emotions, offer comfort and reassurance.
+
+    2. **Contextual Interaction**
+      - Begin with a warm and empathetic welcome message.
+      - Extract precise details from the provided context: {context}.
+      - Respond directly to the user's question: {question}.
+      - Remember the user's name is {first_name}.
+
+    3. **Communication Guidelines**
+      - Maintain a warm, conversational tone.
+      - Use occasional emojis for engagement (e.g., 😊, 🤗, ❤️).
+      - Provide clear, concise, and emotionally supportive information.
+
+    4. **Response Strategies**
+      - Greet users naturally and ask about their wellbeing (e.g., "Welcome, {first_name}! 😊 How are you feeling today?", "Hello {first_name}! 🤗 What's on your mind?").
+      - Always start with a check-in about the user's wellbeing or current situation.
+      - Deliver only relevant information.
+      - Avoid generating content beyond the context.
+      - Handle missing information transparently.
+
+    5. **No Extra Content**
+      - If no information matches the user's request:
         * Respond politely: "I don't have that information at the moment, {first_name}. 😊"
-        * Offer alternative assistance options
-      - Strictly avoid generating unsupported content
-      - Prevent information padding or speculation
+        * Offer alternative assistance options.
+      - Strictly avoid generating unsupported content.
+      - Prevent information padding or speculation.
 
-    5. **Extracting Relevant Links**
+    6. **Extracting Relevant Links**
        - If the user asks for a link related to their request `{question}`, extract the most relevant URL from `{context}` and provide it directly.
        - Example response:
-         - "Here is the link you requested, {first_name}: [URL]"
+         - "Here is the link you requested, [URL]"
 
-    6. **Real-Time Awareness**
-      - Acknowledge current context when appropriate
-      - Stay focused on user's immediate needs
-      - If this is the first message, always ask how the user is feeling and what they would like help with today
+    7. **Real-Time Awareness**
+      - Acknowledge the current context when appropriate.
+      - Stay focused on the user's immediate needs.
+      - If this is the first message, always ask how the user is feeling and what they would like help with today.
 
+  
     **Context:** {context}
     **User's Question:** {question}
-    **Welcome Message:** {welcome_message}
-    **Is First Message:** {is_first_message}
-    **Your Response:**
+    **Your Response:** 
 """)
 
+rag_prompt = PromptTemplate.from_template(template)
 
-
-
-
-
-
-
+retriever = vectorstore.as_retriever()
 
 class OpenRouterLLM:
-    def __init__(self, api_key: str):
-        self.client = OpenAI(
-            base_url="https://openrouter.ai/api/v1",
-            api_key=api
-        )
-        self.headers = {
-            "HTTP-Referer": "http://localhost:3000",
-            "X-Title": "Local Development"
-        }
-
-
+    def __init__(self, key: str):
+        try:
+            self.client = OpenAI(
+                base_url="https://openrouter.ai/api/v1",
+                api_key=key  # Corrected from `key=getmod`
+            )
+            self.headers = {
+                "HTTP-Referer": "http://localhost:3000",
+                "X-Title": "Local Development"
+            }
+        except Exception as e:
+            print(f"Initialization error: {e}")
+            raise
+    
     def stream(self, prompt: str) -> Iterator[str]:
         try:
             completion = self.client.chat.completions.create(
-                extra_headers=self.headers,
                 model="deepseek/deepseek-r1-distill-llama-70b:free",
-                #model="google/gemini-2.0-flash-thinking-exp:free",
                 messages=[{"role": "user", "content": prompt}],
                 stream=True
             )
-
+            
             for chunk in completion:
-                if chunk.choices[0].delta.content is not None:
-                    yield chunk.choices[0].delta.content
+                delta = chunk.choices[0].delta
+                if hasattr(delta, "content") and delta.content:
+                    yield delta.content
         except Exception as e:
-            yield f"Error: {str(e)}"
-
-
-
+            yield f"Streaming error: {str(e)}"
 
 class UserSession:
     def __init__(self):
         self.current_user = None
-        self.is_first_message = True
+        self.welcome_message = None
     
     def set_user(self, user_info):
         self.current_user = user_info
-        self.is_first_message = True
+        self.set_welcome_message(user_info.get("first_name", "Guest"))
     
     def get_user(self):
         return self.current_user
     
-    def mark_message_sent(self):
-        self.is_first_message = False
+    def set_welcome_message(self, first_name):
+        self.welcome_message = (
+            f"<div style='font-size: 18px; font-weight: bold; color: #2E86C1;'>"
+            f"Welcome {first_name}! 👋</div>"
+            f"<div style='font-size: 14px; color: #34495E;'>"
+            f"We appreciate you reaching out to us. You are in a safe and trusted space designed to support you. "
+            f"Here, you can find guidance on gender-based violence (GBV) and legal assistance.<br><br>"
+            f"You don’t have to go through this alone—we are here to listen, support, and help you find the right solutions. "
+            f"You deserve to be heard and helped, and we are committed to standing by your side."
+            f"</div>"
+        )
     
-    def is_first(self):
-        return self.is_first_message
+    def get_welcome_message(self):
+        return self.welcome_message
 
-# Initialize session and LLM
+# Initialize session
 user_session = UserSession()
 
+# Store user details and handle session
+def collect_user_info(first_name, last_name, phone):
+    if not first_name or not last_name or not phone:
+        return "All fields are required to proceed.", gr.update(visible=False), gr.update(visible=True), []
+    
+    # Validate phone number (basic validation)
+    if not phone.replace("+", "").replace("-", "").replace(" ", "").isdigit():
+        return "Please enter a valid phone number.", gr.update(visible=False), gr.update(visible=True), []
+    
+    # Store user info for chat session
+    user_info = {
+        "first_name": first_name,
+        "last_name": last_name,
+        "phone": phone,
+        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
+    }
+    
+    # Set user in session
+    user_session.set_user(user_info)
+    
+    # Generate welcome message
+    welcome_message = user_session.get_welcome_message()
+    
+    # Add initial message to start the conversation
+    chat_history = add_initial_message([(None, welcome_message)])
+    
+    # Return welcome message and update UI
+    return welcome_message, gr.update(visible=True), gr.update(visible=False), chat_history
+
+# Add initial message to start the conversation
+def add_initial_message(chatbot):
+    initial_message = (
+        "<div style='font-size: 14px; font-weight: normal; color: #16A085;'>"
+        f"I just want to check in and see how you are doing." 
+        f"If you are going through something, please know you are not alone, I am here for you, no matter what.🤗"
+        "</div>"
+    )
+    return chatbot + [(None, initial_message)]
+
+# Create RAG chain with user context
+def create_rag_chain(retriever, template, api_key):
+    llm = OpenRouterLLM(api_key)
+    rag_prompt = PromptTemplate.from_template(template)
+
+    def stream_func(input_dict):
+        # Get context using the retriever's invoke method
+        context = retriever.invoke(input_dict["question"])
+        context_str = "\n".join([doc.page_content for doc in context])
+        
+        # Get user info from the session
+        user_info = user_session.get_user() or {}
+        first_name = user_info.get("first_name", "User")
+        
+        # Format prompt with user context
+        prompt = rag_prompt.format(
+            context=context_str,
+            question=input_dict["question"],
+            first_name=first_name
+        )
 
+        # Stream response
+        return llm.stream(prompt)
 
+    return stream_func
 
+def rag_memory_stream(message, history):
+    # Initialize with empty response
+    partial_text = ""
+    
+    # Get user context
+    user_info = user_session.get_user()
+    
+    # Use the rag_chain with the question
+    for new_text in rag_chain({"question": message}):
+        partial_text += new_text
+        yield partial_text
+
+# 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
+
+    # Create the RAG chain with user context
+    global rag_chain
+    rag_chain = create_rag_chain(retriever, template, api_key)
+    
+    # Create theme
+    theme = gr.themes.Soft(
+        primary_hue="indigo",
+        secondary_hue="blue",
+    )
+    
+    with gr.Blocks(theme=theme, css="""
+        .welcome-container { 
+            text-align: center;
+            margin-bottom: 20px;
+            padding: 20px;
+            border-radius: 10px;
+            background-color: #f0f4ff;
+        }
+        .feedback-btn { margin-top: 10px; }
+        footer { margin-top: 30px; text-align: center; }
+    """) as demo:
+        # Welcome banner
+        gr.Markdown("# 🤖 Ijwi ry'Ubufasha - Your AI Assistant", elem_classes=["welcome-container"])
+        
+        # User registration section
+        registration_container = gr.Column(visible=True)
+        with registration_container:
+            gr.Markdown("### Please provide your details to start chatting")
+            
+            with gr.Row():
+                first_name = gr.Textbox(
+                    label="First Name", 
+                    placeholder="Enter your first name",
+                    scale=1
+                )
+                last_name = gr.Textbox(
+                    label="Last Name", 
+                    placeholder="Enter your last name",
+                    scale=1
+                )
+            
+            phone = gr.Textbox(
+                label="Phone Number", 
+                placeholder="Enter your phone number (e.g., +250...)",
+            )
+            
+            with gr.Row():
+                submit_btn = gr.Button("Start Chatting", variant="primary", scale=2)
+            
+            response_message = gr.Markdown(elem_id="welcome-message")
+        
+        # Chatbot section (initially hidden)
+        chatbot_container = gr.Column(visible=False)
+        with chatbot_container:
+            chat_interface = gr.ChatInterface(
+                fn=rag_memory_stream,
+                title="🤖 Help Chatbot",
+                fill_height=True,
+                theme=theme
+            )
+            
+            # Feedback buttons
+            with gr.Row():
+                feedback_label = gr.Markdown("### Was this conversation helpful?")
+            
+            with gr.Row():
+                thumbs_up = gr.Button("👍 Yes, it was helpful", elem_classes=["feedback-btn"])
+                thumbs_down = gr.Button("👎 No, it needs improvement", elem_classes=["feedback-btn"])
+            
+            # Footer with version info
+            gr.Markdown("Ijwi ry'Ubufasha v1.0.0 © 2025", elem_id="footer")
+        
+        # Handle user registration
+        submit_btn.click(
+            collect_user_info, 
+            inputs=[first_name, last_name, phone], 
+            outputs=[response_message, chatbot_container, registration_container, chat_interface.chatbot]
+        )
+        
+        # Handle feedback (placeholder functionality)
+        def record_feedback(feedback_type):
+            # Here you could log feedback to a file or database
+            feedback_message = f"Thank you for your feedback! We'll use it to improve our service."
+            return feedback_message
+        
+        thumbs_up.click(lambda: record_feedback("positive"), outputs=feedback_label)
+        thumbs_down.click(lambda: record_feedback("negative"), outputs=feedback_label)
+    
+    return demo
 
-
+if __name__ == "__main__":
+    # Launch the interface
+    chatbot_interface().launch(share=True, inbrowser=True)