updated files

Dockerfile

@@ -17,5 +17,5 @@ COPY . .
 RUN mkdir -p /code/cache && chmod 777 /code/cache
 
 # Command to run the application
-# We use host 0.0.0.0 and port 7860 (Hugging Face's default port)
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+# Run Gradio directly (compatible with Hugging Face Spaces)
+CMD ["python", "app.py"]

README.md

@@ -3,74 +3,18 @@ title: RAG Analytics Dashboard
 colorFrom: blue
 colorTo: green
 sdk: gradio
-sdk_version: 4.44.1
 app_file: app.py
 pinned: false
 license: apache-2.0
-short_description: Compare RAG system performance across multiple domains
 ---
 
 # RAG Pipeline Analytics Dashboard
 
-Interactive dashboard for analyzing RAG (Retrieval-Augmented Generation) system performance across multiple domains.
-
-## Features
-
-- **Intra-Domain Analysis:** Compare different RAG configurations within a single domain
-- **Performance Metrics:** RMSE (Relevance, Utilization, Completeness), F1 Score, AUC-ROC
-- **Interactive Filtering:** Filter tests by reranker model, summarization model, and chunking strategy
-- **Inter-Domain Comparison:** Compare peak performance across different domains
-- **Data Preview:** Inspect raw data and configuration parameters
-
-## Supported Domains
-
-- **Biomedical** (PubMedQA)
-- **Finance** (FinQA)
-- **General** (MS MARCO)
-- **Legal** (CUAD)
+Interactive dashboard for analyzing RAG system performance across multiple domains (Biomedical, Finance, General, Legal).
 
 ## Usage
 
-1. **Load Data:** Click "Load/Refresh Data" to load all test results
-2. **Select Domain:** Choose a domain from the dropdown
-3. **Apply Filters:** Use the filter dropdowns to compare specific configurations
-4. **View Metrics:** 
-   - RMSE graph shows relevance, utilization, and completeness (lower is better)
-   - Performance graph shows F1 Score and AUC-ROC (higher is better)
-5. **Compare Domains:** Switch to "Inter-Domain Comparison" tab to see overall best configurations
-
-## Interpreting Results
-
-### RMSE Metrics (Lower is Better)
-- **Relevance:** How well retrieved documents match the query
-- **Utilization:** How efficiently the context is used
-- **Completeness:** Coverage of required information
-
-### Performance Metrics (Higher is Better)
-- **F1 Score:** Balance of precision and recall
-- **AUC-ROC:** Overall classification performance
-
-## Configuration Parameters
-
-The dashboard analyzes variations in:
-- Embedding models
-- Reranker models
-- Summarization strategies
-- Chunking strategies
-- Retrieval strategies (Dense, Sparse, Hybrid)
-- Hyperparameters (chunk size, overlap, alpha, top-k)
-
-## Technology Stack
-
-- **Framework:** Gradio 4.0+
-- **Visualization:** Plotly Express
-- **Data Processing:** Pandas
-- **Backend:** FastAPI
-
-## License
-
-Apache 2.0
-
----
+1. Click **Load/Refresh Data** to load test results
+2. Select a domain and apply filters to compare configurations
+3. View RMSE metrics (lower is better) and Performance metrics (higher is better)
 
-**Version:** v2.1.0-fixed | Built for AIML @ IIIT Hyderabad - TalentSprint

app.py

@@ -1,13 +1,11 @@
 import pandas as pd
 import gradio as gr
 import plotly.express as px
-from fastapi import FastAPI
 from typing import Dict
 
 from config import METADATA_COLUMNS, DATA_FOLDER
 from data_loader import load_csv_from_folder, get_available_datasets
 
-app = FastAPI()
 DB: Dict[str, pd.DataFrame] = {}
 
 # --- 1. DATA PROCESSING FUNCTIONS ---
@@ -332,4 +330,6 @@ print(f"Loading data from {DATA_FOLDER}...")
 startup_status = load_data()
 print(startup_status)
 
-app = gr.mount_gradio_app(app, demo, path="/")
+# Launch Gradio app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -1,7 +1,4 @@
 gradio==4.44.1
 huggingface-hub==0.22.2
 plotly>=5.18.0
-pandas>=2.0.0
-fastapi>=0.104.0
-uvicorn[standard]>=0.24.0
-python-multipart>=0.0.6
+pandas>=2.0.0