app.py

# import streamlit as st
# from generator import generate_response_from_document
# from retrieval import retrieve_documents_hybrid
# from evaluation import calculate_metrics
# #from data_processing import load_data_from_faiss
# import time

# # Page Title
# st.title("RAG7 - Real World RAG System")

# # global retrieved_documents
# # retrieved_documents = []

# # global response 
# # response = ""

# # global time_taken_for_response
# # time_taken_for_response = 'N/A'

# # @st.cache_data
# # def load_data():
# #     load_data_from_faiss()

# # data_status = load_data()

# # Question Section
# st.subheader("Hi, What do you want to know today?")
# question = st.text_area("Enter your question:", placeholder="Type your question here...", height=100)

# # # Submit Button
# # if st.button("Submit"):
# #     start_time = time.time()
# #     retrieved_documents = retrieve_documents_hybrid(question, 10)  
# #     response = generate_response_from_document(question, retrieved_documents)
# #     end_time = time.time()
# #     time_taken_for_response = end_time-start_time
# # else:
# #     response = ""

# # # Response Section
# # st.subheader("Response")
# # st.text_area("Generated Response:", value=response, height=150, disabled=True)

# # # Metrics Section
# # st.subheader("Metrics")

# # col1, col2 = st.columns([1, 3])  # Creating two columns for button and metrics display

# # with col1:
# #     if st.button("Calculate Metrics"):
# #         metrics = calculate_metrics(question, response, retrieved_documents, time_taken_for_response)
# #     else:
# #         metrics = ""

# # with col2:
# #     st.text_area("Metrics:", value=metrics, height=100, disabled=True)

# if "retrieved_documents" not in st.session_state:
#     st.session_state.retrieved_documents = []
# if "response" not in st.session_state:
#     st.session_state.response = ""
# if "time_taken_for_response" not in st.session_state:
#     st.session_state.time_taken_for_response = "N/A"

# # Submit Button
# if st.button("Submit"):
#     start_time = time.time()
#     st.session_state.retrieved_documents = retrieve_documents_hybrid(question, 10)  
#     st.session_state.response = generate_response_from_document(question, st.session_state.retrieved_documents)
#     end_time = time.time()
#     st.session_state.time_taken_for_response = end_time - start_time

# # Display stored response
# st.subheader("Response")
# st.text_area("Generated Response:", value=st.session_state.response, height=150, disabled=True)

# col1, col2 = st.columns([1, 3])  # Creating two columns for button and metrics display

# # Calculate Metrics Button
# with col1:
#     if st.button("Calculate Metrics"):
#         metrics = calculate_metrics(question, st.session_state.response, st.session_state.retrieved_documents, st.session_state.time_taken_for_response)
#     else:
#         metrics = {}

# with col2:
#     #st.text_area("Metrics:", value=metrics, height=100, disabled=True)
#     st.json(metrics)

import streamlit as st
import plotly.express as px
from datasets import load_dataset, Dataset, DatasetDict
from generator import generate_response_from_document
from retrieval import retrieve_documents_hybrid
from evaluation import calculate_metrics
import time

# Hugging Face Dataset Details
HF_DATASET_REPO = "cb1716pics/23RAG7_recent_questions"  # Hugging Face repo

# Load Dataset from Hugging Face
@st.cache_resource
def load_hf_dataset():
    try:
        return load_dataset(HF_DATASET_REPO)
    except:
        return DatasetDict({"recent": Dataset.from_dict({"question": [], "response": [], "metrics": []})})

dataset = load_hf_dataset()

# Function to Save Data to Hugging Face Dataset
def save_to_hf_dataset(question, response, metrics):
    global dataset
    new_data = {
        "question": [question],
        "response": [response],
        "metrics": [metrics]
    }
    
    # Convert existing dataset to a list and append new data
    dataset_dict = dataset["recent"].to_dict()
    for key in new_data.keys():
        dataset_dict[key] = dataset_dict.get(key, []) + new_data[key]
    
    # Keep only the last 10 entries
    for key in dataset_dict.keys():
        dataset_dict[key] = dataset_dict[key][-10:]

    # Convert back to dataset and push to Hugging Face
    dataset["recent"] = Dataset.from_dict(dataset_dict)
    dataset.push_to_hub(HF_DATASET_REPO)

# Streamlit UI
st.title("🔍 RAG7 - Real World RAG System")

# Sidebar - Recent Questions
st.sidebar.header("📌 Recent Questions")
if len(dataset["recent"]) > 0:
    for q in dataset["recent"]["question"][-10:]:
        st.sidebar.write(f"🔹 {q}")

# Sidebar - Analytics with Graph
st.sidebar.header("📊 Analytics Overview")
if len(dataset["recent"]) > 0:
    # Extract recent metrics for visualization
    metrics_data = dataset["recent"]["metrics"][-10:]
    metrics_keys = ["context_relevance", "context_utilization", "completeness", "adherence"]
    
    # Prepare a dictionary for graphing
    graph_data = {key: [m[key] for m in metrics_data] for key in metrics_keys}
    graph_data["Question #"] = list(range(1, len(metrics_data) + 1))

    # Convert to DataFrame for Plotly
    import pandas as pd
    df = pd.DataFrame(graph_data)

    # Plot Metrics Over Time
    fig = px.line(df, x="Question #", y=metrics_keys, 
                  labels={"value": "Score", "variable": "Metric"},
                  title="📈 Model Performance Over Recent Questions")
    st.sidebar.plotly_chart(fig, use_container_width=True)

# Evaluate Button
if st.sidebar.button("⚡ Evaluate RAG Model"):
    st.sidebar.success("✅ Model Evaluation Triggered!")

# Main Section - User Input
st.subheader("💬 Ask a Question")
question = st.text_area("Enter your question:", placeholder="Type your question here...", height=100)

# Submit Button
if st.button("🚀 Submit"):
    start_time = time.time()
    retrieved_documents = retrieve_documents_hybrid(question, 10)  
    response = generate_response_from_document(question, retrieved_documents)
    end_time = time.time()
    time_taken_for_response = end_time - start_time

    # Calculate Metrics
    metrics = calculate_metrics(question, response, retrieved_documents, time_taken_for_response)

    # Save Data
    save_to_hf_dataset(question, response, metrics)

    # Display Response
    st.subheader("💡 Response")
    st.text_area("Generated Response:", value=response, height=150, disabled=True)

    # Display Metrics with Bar Chart
    st.subheader("📊 Metrics")
    st.json(metrics)

    # Plot Bar Chart for Metrics
    metric_df = pd.DataFrame({"Metric": list(metrics.keys()), "Score": list(metrics.values())})
    fig2 = px.bar(metric_df, x="Metric", y="Score", title="📊 Current Query Metrics")
    st.plotly_chart(fig2, use_container_width=True)