Update app.py

app.py

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+import streamlit as st
+import pandas as pd
+import os
+import base64
+
+# Import evaluation modules
+from phoenix_code import phoenix_eval
+from ragas_code import ragas_eval
+from traditional_metrics_score import RAGEvaluator
+
+# Set page configuration
+st.set_page_config(
+    page_title="RAG Evaluation Toolkit",
+    page_icon="🔍",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for improved styling
+def local_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+# Function to create a more visually appealing file uploader
+def custom_file_uploader():
+    st.markdown("""
+    <div class="file-upload-container">
+        <div class="file-upload-icon">📂</div>
+        <div class="file-upload-text">
+            Drag and Drop or <span class="file-upload-browse">Browse Files</span>
+        </div>
+        <small>Supports CSV, XLS, XLSX</small>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    uploaded_file = st.file_uploader(
+        "Upload Dataset", 
+        type=["csv", "xls", "xlsx"], 
+        label_visibility="collapsed"
+    )
+    return uploaded_file
+
+# Main Streamlit App
+def main():
+    # Custom CSS for enhanced styling
+    st.markdown("""
+    <style>
+    .stApp {
+        background-color: #f0f2f6;
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    }
+    .stTitle {
+        color: #2C3E50;
+        text-align: center;
+        margin-bottom: 30px;
+    }
+    .stMarkdown {
+        color: #34495E;
+    }
+    .stButton>button {
+        background-color: #3498DB;
+        color: white;
+        border: none;
+        border-radius: 6px;
+        padding: 10px 20px;
+        transition: all 0.3s ease;
+    }
+    .stButton>button:hover {
+        background-color: #2980B9;
+        transform: scale(1.05);
+    }
+    .sidebar .sidebar-content {
+        background-color: #FFFFFF;
+        border-radius: 10px;
+        padding: 20px;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+    }
+    .file-upload-container {
+        border: 2px dashed #3498DB;
+        border-radius: 10px;
+        padding: 30px;
+        text-align: center;
+        background-color: #FFFFFF;
+        transition: all 0.3s ease;
+    }
+    .file-upload-container:hover {
+        border-color: #2980B9;
+        background-color: #F1F8FF;
+    }
+    .file-upload-icon {
+        font-size: 50px;
+        color: #3498DB;
+        margin-bottom: 15px;
+    }
+    .file-upload-text {
+        color: #2C3E50;
+        font-size: 18px;
+    }
+    .file-upload-browse {
+        color: #3498DB;
+        font-weight: bold;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+    # App Title
+    st.markdown("<h1 class='stTitle'>🔍 RAG Evaluation Toolkit</h1>", unsafe_allow_html=True)
+    
+    # Sidebar for Configuration
+    st.sidebar.header("📋 Evaluation Configuration")
+    
+    # API Key Input with improved styling
+    st.sidebar.subheader("OpenAI API Key")
+    openai_api_key = st.sidebar.text_input(
+        "Enter your OpenAI API Key", 
+        type="password", 
+        help="Required for running evaluations"
+    )
+    
+    # File Upload Section
+    st.markdown("### 📊 Upload Your Dataset")
+    uploaded_file = custom_file_uploader()
+    
+    # Evaluation Type Selection
+    st.sidebar.subheader("🛠 Evaluation Methods")
+    evaluation_methods = {
+        "Phoenix Evaluation": [
+            "hallucination", 
+            "toxicity", 
+            "relevance", 
+            "Q&A"
+        ],
+        "RAGAS Evaluation": [
+            "answer_correctness", 
+            "answer_relevancy", 
+            "faithfulness", 
+            "context_precision", 
+            "context_recall", 
+            "context_relevancy", 
+            "answer_similarity"
+        ],
+        "Traditional Metrics": [
+            "BLEU", 
+            "ROUGE-1", 
+            "BERT Score", 
+            "Perplexity", 
+            "Diversity", 
+            "Racial Bias"
+        ]
+    }
+    
+    # Multiselect for each evaluation method
+    selected_metrics = {}
+    for method, metrics in evaluation_methods.items():
+        if st.sidebar.checkbox(method):
+            selected_metrics[method] = st.sidebar.multiselect(
+                f"Select {method} Metrics", 
+                metrics
+            )
+    
+    # Evaluation Button
+    if uploaded_file and openai_api_key and selected_metrics:
+        if st.button("🚀 Run Evaluation"):
+            # Load data
+            file_extension = os.path.splitext(uploaded_file.name)[1]
+            if file_extension.lower() == ".csv":
+                df = pd.read_csv(uploaded_file)
+            elif file_extension.lower() in [".xls", ".xlsx"]:
+                df = pd.read_excel(uploaded_file)
+            
+            # Combine results
+            combined_results = pd.DataFrame()
+            
+            # Progress bar
+            progress_bar = st.progress(0)
+            
+            # Run evaluations
+            with st.spinner("Processing evaluations..."):
+                # Phoenix Evaluation
+                if "Phoenix Evaluation" in selected_metrics:
+                    progress_bar.progress(33)
+                    phoenix_results = phoenix_eval(
+                        selected_metrics.get("Phoenix Evaluation", []), 
+                        openai_api_key, 
+                        df.copy()
+                    )
+                    combined_results = pd.concat([combined_results, phoenix_results], axis=1)
+                
+                # RAGAS Evaluation
+                if "RAGAS Evaluation" in selected_metrics:
+                    progress_bar.progress(66)
+                    ragas_results = ragas_eval(
+                        selected_metrics.get("RAGAS Evaluation", []), 
+                        openai_api_key, 
+                        df.copy()
+                    )
+                    combined_results = pd.concat([combined_results, ragas_results], axis=1)
+                
+                # Traditional Metrics Evaluation
+                if "Traditional Metrics" in selected_metrics:
+                    progress_bar.progress(100)
+                    traditional_results = RAGEvaluator(
+                        df=df.copy(), 
+                        selected_metrics=selected_metrics.get("Traditional Metrics", [])
+                    )
+                    combined_results = pd.concat([combined_results, traditional_results], axis=1)
+                
+                # Save results
+                results_filename = "rag_evaluation_results.xlsx"
+                combined_results.to_excel(results_filename, index=False)
+                
+                # Success message and download button
+                st.success("Evaluation Completed Successfully!")
+                
+                # Create download button with improved styling
+                with open(results_filename, "rb") as file:
+                    btn = st.download_button(
+                        label="📥 Download Evaluation Results",
+                        data=file,
+                        file_name=results_filename,
+                        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+                    )
+                
+                # Display results preview
+                st.markdown("### 📊 Results Preview")
+                st.dataframe(combined_results)
+
+# Run the app
+if __name__ == "__main__":
+    main()