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app.py

@@ -1,88 +1,196 @@
-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)
+# 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()
-#     retrieved_documents = retrieve_documents_hybrid(question, 10)  
-#     response = generate_response_from_document(question, retrieved_documents)
+#     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()
-#     time_taken_for_response = end_time-start_time
-# else:
-#     response = ""
+#     st.session_state.time_taken_for_response = end_time - start_time
 
-# # Response Section
+# # Display stored response
 # st.subheader("Response")
-# st.text_area("Generated Response:", value=response, height=150, disabled=True)
-
-# # Metrics Section
-# st.subheader("Metrics")
+# 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, response, retrieved_documents, time_taken_for_response)
+#         metrics = calculate_metrics(question, st.session_state.response, st.session_state.retrieved_documents, st.session_state.time_taken_for_response)
 #     else:
-#         metrics = ""
+#         metrics = {}
 
 # with col2:
-#     st.text_area("Metrics:", value=metrics, height=100, disabled=True)
+#     #st.text_area("Metrics:", value=metrics, height=100, disabled=True)
+#     st.json(metrics)
 
-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"
+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"):
+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)
+    retrieved_documents = retrieve_documents_hybrid(question, 10)  
+    response = generate_response_from_document(question, retrieved_documents)
     end_time = time.time()
-    st.session_state.time_taken_for_response = end_time - start_time
+    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)
+    # Calculate Metrics
+    metrics = calculate_metrics(question, response, retrieved_documents, time_taken_for_response)
 
-col1, col2 = st.columns([1, 3])  # Creating two columns for button and metrics display
+    # Save Data
+    save_to_hf_dataset(question, response, metrics)
 
-# 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 = {}
+    # Display Response
+    st.subheader("💡 Response")
+    st.text_area("Generated Response:", value=response, height=150, disabled=True)
 
-with col2:
-    #st.text_area("Metrics:", value=metrics, height=100, 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)

requirements.txt

@@ -15,4 +15,5 @@ nltk
 requests
 rouge-score
 numpy
-rank_bm25
+plotly
+huggingface_hub