import streamlit as st import pandas as pd import io import base64 import time import tempfile from datetime import datetime from backend1_integration import chat_with_agents, build_index # (logic unchanged) # ---------------------------------- # Page configuration # ---------------------------------- st.set_page_config( page_title="InsightPilot - AI PowerBI Report Assistant", page_icon="🚀", layout="wide", initial_sidebar_state="collapsed" ) # ---------------------------------- # CSS – compact, professional, boxed sections # ---------------------------------- st.markdown(""" """, unsafe_allow_html=True) # ---------------------------------- # Small helpers # ---------------------------------- def create_download_link(file_path, file_name): try: with open(file_path, "rb") as f: b64 = base64.b64encode(f.read()).decode() return f'📥 Download Report PDF' except FileNotFoundError: return "❌ File not found. Please try generating the report again." def extract_pdf_text(pdf_file): try: import fitz # PyMuPDF with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp: tmp.write(pdf_file.read()) path = tmp.name doc = fitz.open(path) return "\n".join([p.get_text() for p in doc]) except Exception as e: st.error(f"Error reading PDF: {str(e)}") return None # ---------------------------------- # App # ---------------------------------- def main(): # ---- Header (now compact) ---- st.markdown("""
🚀 InsightPilot
Your AI PowerBI Report Assistant
""", unsafe_allow_html=True) with st.container(): st.markdown('
', unsafe_allow_html=True) # ---- Session state ---- for k, v in { 'analysis_complete': False, 'analysis_result': None, 'pdf_path': None, 'file_type': None, 'uploaded_file_name': None }.items(): st.session_state.setdefault(k, v) # ---- Select type ---- st.markdown('
📁 Select Analysis Type
', unsafe_allow_html=True) col1, col2 = st.columns(2) with col1: csv_selected = st.button("📊 CSV Dataset Analysis", use_container_width=True) with col2: pdf_selected = st.button("📋 PDF Report Analysis", use_container_width=True) if csv_selected: st.session_state.file_type = "csv" if pdf_selected: st.session_state.file_type = "pdf" # ---- Upload box (small) ---- if st.session_state.file_type: st.markdown(f"""

Upload your {st.session_state.file_type.upper()} file

""", unsafe_allow_html=True) if st.session_state.file_type == "csv": uploaded_file = st.file_uploader( "Choose a CSV file", type=['csv'], help="Upload your file for AI analysis.", label_visibility="collapsed", key="csv_uploader" ) else: uploaded_file = st.file_uploader( "Choose a PDF file", type=['pdf'], help="Upload your file for AI analysis.", label_visibility="collapsed", key="pdf_uploader" ) if uploaded_file is not None: st.session_state.uploaded_file_name = uploaded_file.name size_mb = len(uploaded_file.getvalue()) / 1024 / 1024 st.markdown(f"""
✅ File uploaded successfully!
📄 Name: {uploaded_file.name}
📏 Size: {size_mb:.2f} MB
🗂️ Type: {st.session_state.file_type.upper()} Analysis
""", unsafe_allow_html=True) # ---- CSV preview - inside a card ---- if st.session_state.file_type == "csv": try: df = pd.read_csv(uploaded_file, encoding='latin-1') st.markdown('
', unsafe_allow_html=True) st.markdown("### 👀 Dataset Preview") st.write(f"**Shape:** {df.shape[0]} rows × {df.shape[1]} columns") st.dataframe(df.head(), use_container_width=True) st.markdown('
', unsafe_allow_html=True) except Exception as e: st.error(f"Error reading CSV file: {str(e)}") # ---- Start analysis button ---- if st.button("🔍 Start AI Analysis", use_container_width=True): with st.spinner("🔧 Initializing AI systems and analyzing your data..."): try: query_engine = build_index() if st.session_state.file_type == "csv": file_content = io.BytesIO(uploaded_file.getvalue()) else: pdf_text = extract_pdf_text(uploaded_file) if pdf_text is None: st.error("Failed to extract text from PDF. Please try again.") st.stop() file_content = pdf_text result, pdf_path = chat_with_agents( file_type=st.session_state.file_type, file_content=file_content, query_engine=query_engine ) st.session_state.analysis_result = result st.session_state.pdf_path = pdf_path st.session_state.analysis_complete = True st.success("✅ Analysis complete!") except Exception as e: st.markdown(f"""
❌ Analysis failed: {str(e)}
""", unsafe_allow_html=True) # ---- Results (inside cards) ---- if st.session_state.analysis_complete and st.session_state.analysis_result: st.markdown("---") st.markdown('
', unsafe_allow_html=True) st.markdown("## 📊 Analysis Results") st.markdown("### 🎯 AI-Generated Insights") st.markdown(st.session_state.analysis_result) st.markdown('
', unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) st.markdown("### 📋 Generated Report Preview") lines = str(st.session_state.analysis_result).split('\n') preview_lines = [l for l in lines if l.strip() and not l.startswith(' ')][:5] preview_text = '\n'.join(preview_lines) st.markdown(f""" **Report Summary:** {preview_text} **Generated:** {datetime.now().strftime('%B %d, %Y at %I:%M %p')} **File Analyzed:** {st.session_state.uploaded_file_name} **Analysis Type:** {st.session_state.file_type.upper()} """) st.markdown('
', unsafe_allow_html=True) col1, col2, col3 = st.columns([1,2,1]) with col2: if st.session_state.pdf_path: with open(st.session_state.pdf_path, "rb") as pdf_file: st.download_button( label="📥 Download Full Report (PDF)", data=pdf_file, file_name=f"insightpilot_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf", mime="application/pdf", use_container_width=True ) st.markdown("---") col1, col2, col3 = st.columns([1,1,1]) with col2: if st.button("🔄 Start New Analysis", use_container_width=True): for key in [ 'analysis_complete', 'analysis_result', 'pdf_path', 'file_type', 'uploaded_file_name' ]: if key in st.session_state: del st.session_state[key] st.rerun() # footer st.markdown("""
🚀 InsightPilot • Powered by CrewAI & OpenAI
""", unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) # end container-narrow if __name__ == "__main__": main()