Update app.py

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 import os
 import requests
+from langdetect import detect
 
 # Load the Hugging Face token from environment variables (secrets)
 token = os.environ.get("Key2")  # Replace "KEY2" with your secret key name
@@ -25,20 +26,78 @@ def query_huggingface_api(prompt, max_new_tokens=50, temperature=0.7, top_k=50):
         st.error(f"Error: {response.status_code} - {response.text}")
         return None
 
+# Function to detect language
+def detect_language(text):
+    try:
+        return detect(text)
+    except:
+        return "en"  # Default to English if detection fails
+
 # Streamlit App
 def main():
-    st.title("Hugging Face API Test")
+    st.title("RAG Model with Advanced Query Translation and Indexing")
     st.write("Enter a prompt and get a response from the model.")
 
-    # Input prompt
+    # Sidebar for options
+    st.sidebar.title("Options")
+
+    # Query Translation Options
+    st.sidebar.header("Query Translation")
+    query_translation = st.sidebar.selectbox(
+        "Select Query Translation Method",
+        ["Multi-Query", "RAG Fusion", "Decomposition", "Step Back", "HyDE"]
+    )
+
+    # Indexing Options
+    st.sidebar.header("Indexing")
+    indexing_method = st.sidebar.selectbox(
+        "Select Indexing Method",
+        ["Multi-Representation", "Raptors", "ColBERT"]
+    )
+
+    # LLM Parameters
+    st.sidebar.header("LLM Parameters")
+    max_new_tokens = st.sidebar.slider("Max New Tokens", 10, 100, 50)
+    temperature = st.sidebar.slider("Temperature", 0.1, 1.0, 0.7)
+    top_k = st.sidebar.slider("Top K", 1, 100, 50)
+
+    # System Prompt
+    st.sidebar.header("System Prompt")
+    default_system_prompt = "You are a helpful assistant."
+    system_prompt = st.sidebar.text_area("System Prompt", default_system_prompt)
+
+    # Main Content
+    st.header("Input Prompt")
     prompt = st.text_input("Enter your prompt:")
     if prompt:
         st.write("**Prompt:**", prompt)
 
+        # Detect Language
+        language = detect_language(prompt)
+        st.write(f"**Detected Language:** {language}")
+
+        # Query Translation
+        if st.button("Apply Query Translation"):
+            st.write(f"**Applied Query Translation Method:** {query_translation}")
+            # Implement query translation logic here
+            # Example: Generate multiple queries for Multi-Query
+            if query_translation == "Multi-Query":
+                queries = [f"{prompt} - Query {i}" for i in range(3)]
+                st.write("**Generated Queries:**", queries)
+
+        # Indexing
+        if st.button("Apply Indexing"):
+            st.write(f"**Applied Indexing Method:** {indexing_method}")
+            # Implement indexing logic here
+            # Example: Indexing with ColBERT
+            if indexing_method == "ColBERT":
+                st.write("Indexing with ColBERT...")
+
         # Query the Hugging Face API
-        response = query_huggingface_api(prompt)
-        if response:
-            st.write("**Response:**", response)
+        if st.button("Generate Response"):
+            response = query_huggingface_api(prompt, max_new_tokens, temperature, top_k)
+            if response:
+                st.write("**Response:**", response)
 
 if __name__ == "__main__":
     main()