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| #!/usr/bin/env python3 | |
| """ | |
| app.py — Streamlit UI for CPI Tender Matcher | |
| Enhanced with: | |
| - Agentic resoning for intelligent match explanations | |
| - Custom profile input: manual form OR PDF upload | |
| - Existing pre-built profile selection preserved | |
| Deploy on Streamlit Community Cloud: https://streamlit.io/cloud | |
| Run locally: streamlit run app.py | |
| """ | |
| import sys | |
| import json | |
| import time | |
| import io | |
| from pathlib import Path | |
| import streamlit as st | |
| sys.path.insert(0, str(Path(__file__).parent)) | |
| from src.parser import load_tenders, load_profiles | |
| from src.ranker import TenderRanker, get_top_disqualifier | |
| from src.summarizer import generate_summary | |
| from src.utils import get_profile_language, format_budget, ensure_dir | |
| # ── Optional imports (graceful fallback if not installed) ────────────────────── | |
| try: | |
| from groq import Groq | |
| GROQ_AVAILABLE = True | |
| except ImportError: | |
| GROQ_AVAILABLE = False | |
| try: | |
| import pypdf | |
| PYPDF_AVAILABLE = True | |
| except ImportError: | |
| PYPDF_AVAILABLE = False | |
| # ─── Config ─────────────────────────────────────────────────────────────────── | |
| import os | |
| def _load_groq_api_key() -> str: | |
| """Load GROQ_API_KEY from Streamlit secrets, then environment variable fallback.""" | |
| # 1. Streamlit Cloud secrets (st.secrets) | |
| try: | |
| key = st.secrets["GROQ_API_KEY"] | |
| if key: | |
| return key | |
| except (KeyError, FileNotFoundError): | |
| pass | |
| # 2. Local environment variable (e.g. from a .env file loaded externally) | |
| key = os.environ.get("GROQ_API_KEY", "") | |
| return key | |
| GROQ_API_KEY = _load_groq_api_key() | |
| GROQ_MODEL = "openai/gpt-oss-20b" | |
| # Disable LLM features silently if the key was not found | |
| GROQ_AVAILABLE = GROQ_AVAILABLE and bool(GROQ_API_KEY) | |
| SECTORS = ["agritech", "healthtech", "cleantech", "edtech", "fintech", "wastetech", "general"] | |
| REGIONS = ["East Africa", "West Africa", "Central Africa", "Southern Africa", "Africa"] | |
| # ─── Page Config ────────────────────────────────────────────────────────────── | |
| st.set_page_config( | |
| page_title="CPI Tender Matcher", | |
| page_icon="🌍", | |
| layout="wide", | |
| ) | |
| # ─── Load tender data once (cached) ─────────────────────────────────────────── | |
| def load_data(): | |
| tenders = load_tenders("data/tenders") | |
| profiles = load_profiles("data/profiles.json") | |
| ranker = TenderRanker(tenders) | |
| return tenders, profiles, ranker | |
| TENDERS, PROFILES, RANKER = load_data() | |
| PROFILE_MAP = {p["id"]: p for p in PROFILES} | |
| PROFILE_CHOICES = [f"{p['id']} — {p['name']} ({p['country']})" for p in PROFILES] | |
| # ─── Groq LLM helper ────────────────────────────────────────────────────────── | |
| def groq_explain_match(profile, tender, rank, score, breakdown, language="en"): | |
| """ | |
| Use Groq (openai/gpt-oss-20b) to generate a rich, personalised match explanation. | |
| Falls back to the template-based summarizer if Groq is unavailable or fails. | |
| """ | |
| if not GROQ_AVAILABLE: | |
| return generate_summary(profile, tender, rank, score, breakdown, language) | |
| lang_instruction = "Respond in French." if language == "fr" else "Respond in English." | |
| system_prompt = ( | |
| "You are an expert grant advisor helping African cooperatives and startups " | |
| "find the best-matched tenders and grants. Your explanations are concise, " | |
| "actionable, and encouraging. Always respond in at most 100 words." | |
| ) | |
| user_prompt = ( | |
| f"Explain why this tender is a great (or partial) match for the organization below.\n" | |
| f"{lang_instruction}\n\n" | |
| f"## Organization Profile\n" | |
| f"- Name: {profile.get('name')}\n" | |
| f"- Sector: {profile.get('sector')}\n" | |
| f"- Country: {profile.get('country')}\n" | |
| f"- Employees: {profile.get('employees', 'N/A')}\n" | |
| f"- Needs: {profile.get('needs_text', '')}\n" | |
| f"- Budget capacity: USD {profile.get('budget_max', 0):,}\n\n" | |
| f"## Matched Tender (Rank #{rank})\n" | |
| f"- Title: {tender.get('title')}\n" | |
| f"- Sector: {tender.get('sector')}\n" | |
| f"- Budget: USD {tender.get('budget', 0):,}\n" | |
| f"- Deadline: {tender.get('deadline')}\n" | |
| f"- Region: {tender.get('region')}\n\n" | |
| f"## Scoring Breakdown\n" | |
| f"- Composite score: {score:.4f}/1.0\n" | |
| f"- TF-IDF similarity: {breakdown.get('tfidf_similarity', 0):.2%}\n" | |
| f"- Sector match: {breakdown.get('sector_match', 0):.2%}\n" | |
| f"- Budget fit: {breakdown.get('budget_score', 0):.2%}\n" | |
| f"- Deadline urgency: {breakdown.get('urgency_score', 0):.2%}\n\n" | |
| f"Write a compelling max-100-word explanation and suggest one next step." | |
| ) | |
| try: | |
| client = Groq(api_key=GROQ_API_KEY) | |
| response = client.chat.completions.create( | |
| model=GROQ_MODEL, | |
| messages=[ | |
| {"role": "system", "content": system_prompt}, | |
| {"role": "user", "content": user_prompt}, | |
| ], | |
| max_tokens=180, | |
| temperature=0.7, | |
| ) | |
| return response.choices[0].message.content.strip() | |
| except Exception as e: | |
| st.warning(f"Groq LLM unavailable ({e}); using template summary.") | |
| return generate_summary(profile, tender, rank, score, breakdown, language) | |
| def groq_parse_pdf_profile(pdf_text): | |
| """ | |
| Ask Groq to extract a structured profile from raw PDF text. | |
| Returns a dict matching the profiles.json schema. | |
| """ | |
| if not GROQ_AVAILABLE: | |
| return {} | |
| system_prompt = ( | |
| "You are a data extraction assistant. Extract organisation profile information " | |
| "from the text and return ONLY valid JSON — no markdown, no explanation." | |
| ) | |
| user_prompt = ( | |
| "Extract the following fields from the document text below and return a JSON object.\n" | |
| "If a field is not found, use a sensible default.\n\n" | |
| "Required JSON keys:\n" | |
| " id — use \"custom_pdf\"\n" | |
| " name — organisation name (string)\n" | |
| " sector — one of: agritech, healthtech, cleantech, edtech, fintech, wastetech, general\n" | |
| " country — country name (string)\n" | |
| " employees — number of employees (integer, default 0)\n" | |
| " languages — list of language codes, e.g. [\"en\"] or [\"fr\"]\n" | |
| " needs_text — 1-3 sentence description of what funding is needed for\n" | |
| " past_funding — past funding received in USD (integer, default 0)\n" | |
| " budget_max — maximum grant amount sought in USD (integer, default 50000)\n" | |
| " region — one of: East Africa, West Africa, Central Africa, Southern Africa, Africa\n\n" | |
| f"Document text:\n\"\"\"\n{pdf_text[:4000]}\n\"\"\"\n\n" | |
| "Return ONLY the JSON object." | |
| ) | |
| try: | |
| client = Groq(api_key=GROQ_API_KEY) | |
| response = client.chat.completions.create( | |
| model=GROQ_MODEL, | |
| messages=[ | |
| {"role": "system", "content": system_prompt}, | |
| {"role": "user", "content": user_prompt}, | |
| ], | |
| max_tokens=500, | |
| temperature=0.2, | |
| ) | |
| raw = response.choices[0].message.content.strip() | |
| raw = raw.strip("```json").strip("```").strip() | |
| return json.loads(raw) | |
| except Exception as e: | |
| st.error(f"Could not parse profile from PDF: {e}") | |
| return {} | |
| def extract_pdf_text(uploaded_file): | |
| """Extract raw text from an uploaded PDF file.""" | |
| if not PYPDF_AVAILABLE: | |
| return "" | |
| reader = pypdf.PdfReader(io.BytesIO(uploaded_file.read())) | |
| pages = [page.extract_text() or "" for page in reader.pages] | |
| return "\n".join(pages) | |
| # ─── Header ─────────────────────────────────────────────────────────────────── | |
| st.title("🌍 CPI Tender Matcher") | |
| st.markdown( | |
| "**Multilingual Grant Finder for African Cooperatives** \n" | |
| "AIMS KTT Hackathon · T2.2 | Author: Samson Niyizurugero \n" | |
| "Supports English 🇬🇧 and French 🇫🇷 ·" | |
| ) | |
| if GROQ_AVAILABLE: | |
| st.success(f" Groq LLM active — model: `{GROQ_MODEL}`") | |
| else: | |
| st.warning(" `groq` package not installed. Run `pip install groq` to enable LLM explanations.") | |
| st.divider() | |
| # ─── Sidebar: Settings ──────────────────────────────────────────────────────── | |
| with st.sidebar: | |
| st.header("⚙️ Settings") | |
| language = st.selectbox( | |
| "Output Language", | |
| options=["EN", "FR"], | |
| help="Language for match explanations", | |
| ) | |
| top_k = st.slider( | |
| "Top-K Results", | |
| min_value=1, max_value=10, value=5, step=1, | |
| help="Number of tenders to return", | |
| ) | |
| use_llm = st.toggle( | |
| "Use Groq LLM Explanations", | |
| value=True, | |
| help="Use Groq AI for richer explanations (slower but smarter)", | |
| ) | |
| st.divider() | |
| st.markdown("### 📖 How It Works") | |
| st.markdown( | |
| "1. **Profile** — Select an existing profile, fill in the form, or upload a PDF \n" | |
| "2. **Parse** — Tenders parsed from TXT/HTML/PDF, language detected \n" | |
| "3. **Rank** — Hybrid scoring: `0.45×TF-IDF + 0.25×Sector + 0.20×Budget + 0.10×Urgency` \n" | |
| "4. **Explain** — Groq LLM generates personalised explanations \n" | |
| "5. **Deploy** — Designed for rural cooperatives via WhatsApp/SMS/voice" | |
| ) | |
| # ─── Profile Input — Three Modes ────────────────────────────────────────────── | |
| st.subheader("👤 Business Profile") | |
| profile_mode = st.radio( | |
| "How would you like to provide your profile?", | |
| options=["📋 Select existing profile", "✏️ Fill in manually", "📄 Upload PDF"], | |
| horizontal=True, | |
| ) | |
| profile = None | |
| # ── Mode 1: Select existing ──────────────────────────────────────────────────── | |
| if profile_mode == "📋 Select existing profile": | |
| profile_choice = st.selectbox( | |
| "Select Business Profile", | |
| options=[""] + PROFILE_CHOICES, | |
| help="Choose a pre-loaded cooperative or business profile", | |
| ) | |
| if profile_choice: | |
| profile_id = profile_choice.split("—")[0].strip() | |
| profile = PROFILE_MAP.get(profile_id) | |
| if profile: | |
| with st.expander("👁️ Profile Details", expanded=True): | |
| c1, c2, c3, c4 = st.columns(4) | |
| c1.metric("Name", profile.get("name")) | |
| c2.metric("Sector", profile.get("sector")) | |
| c3.metric("Country", profile.get("country")) | |
| c4.metric("Employees", profile.get("employees", "—")) | |
| st.markdown(f"**Languages:** {', '.join(profile.get('languages', ['en'])).upper()}") | |
| st.markdown(f"**Needs:** {profile.get('needs_text', '')}") | |
| # ── Mode 2: Manual form ──────────────────────────────────────────────────────── | |
| elif profile_mode == "✏️ Fill in manually": | |
| with st.form("manual_profile_form"): | |
| st.markdown("#### Enter your organisation details") | |
| col_a, col_b = st.columns(2) | |
| with col_a: | |
| m_name = st.text_input("Organisation Name *", placeholder="e.g. AgriGrow Rwanda") | |
| m_country = st.text_input("Country *", placeholder="e.g. Rwanda") | |
| m_sector = st.selectbox("Sector *", SECTORS) | |
| m_region = st.selectbox("Region *", REGIONS) | |
| with col_b: | |
| m_employees = st.number_input("Number of Employees", min_value=0, value=10) | |
| m_budget_max = st.number_input("Max Grant Sought (USD)", min_value=0, value=50000, step=5000) | |
| m_past_fund = st.number_input("Past Funding Received (USD)", min_value=0, value=0, step=1000) | |
| m_lang = st.multiselect("Languages", ["en", "fr"], default=["en"]) | |
| m_needs = st.text_area( | |
| "Describe your funding needs *", | |
| placeholder="e.g. We need funding to scale our precision farming app...", | |
| height=100, | |
| ) | |
| submitted = st.form_submit_button("✅ Use This Profile", type="primary") | |
| if submitted: | |
| if not m_name or not m_country or not m_needs: | |
| st.error("Please fill in the required fields: Name, Country, and Needs.") | |
| else: | |
| profile = { | |
| "id": "custom_manual", | |
| "name": m_name, | |
| "sector": m_sector, | |
| "country": m_country, | |
| "employees": int(m_employees), | |
| "languages": m_lang or ["en"], | |
| "needs_text": m_needs, | |
| "past_funding": int(m_past_fund), | |
| "budget_max": int(m_budget_max), | |
| "region": m_region, | |
| } | |
| st.session_state["manual_profile"] = profile | |
| st.success(f"✅ Profile set: **{m_name}** ({m_sector}, {m_country})") | |
| if "manual_profile" in st.session_state and profile is None: | |
| profile = st.session_state["manual_profile"] | |
| st.info(f"Using saved manual profile: **{profile['name']}**") | |
| # ── Mode 3: PDF Upload ───────────────────────────────────────────────────────── | |
| elif profile_mode == "📄 Upload PDF": | |
| if not PYPDF_AVAILABLE: | |
| st.error("`pypdf` is not installed. Run `pip install pypdf` to enable PDF upload.") | |
| elif not GROQ_AVAILABLE: | |
| st.error("`groq` is not installed. Run `pip install groq` to enable PDF profile extraction.") | |
| else: | |
| uploaded_pdf = st.file_uploader( | |
| "Upload your organisation profile as a PDF", | |
| type=["pdf"], | |
| help="The AI will extract your organisation details automatically", | |
| ) | |
| if uploaded_pdf: | |
| if st.button("🤖 Extract Profile from PDF", type="primary"): | |
| with st.spinner("Groq AI is reading your PDF..."): | |
| pdf_text = extract_pdf_text(uploaded_pdf) | |
| if not pdf_text.strip(): | |
| st.error("Could not extract text from the PDF. Try a text-based PDF.") | |
| else: | |
| extracted = groq_parse_pdf_profile(pdf_text) | |
| if extracted: | |
| profile = extracted | |
| st.session_state["pdf_profile"] = profile | |
| st.success("✅ Profile extracted successfully!") | |
| with st.expander("📋 Extracted Profile", expanded=True): | |
| st.json(profile) | |
| else: | |
| st.error("Could not extract a profile. Try filling in the form manually.") | |
| if "pdf_profile" in st.session_state and profile is None: | |
| profile = st.session_state["pdf_profile"] | |
| st.info(f"Using PDF-extracted profile: **{profile.get('name', 'Unknown')}**") | |
| st.divider() | |
| # ─── Match Button ───────────────────────────────────────────────────────────── | |
| match_btn = st.button("🔍 Find Matching Tenders", type="primary", use_container_width=True) | |
| # ─── Main Results ───────────────────────────────────────────────────────────── | |
| if match_btn: | |
| if not profile: | |
| st.warning("Please provide a business profile first (select, fill in, or upload).") | |
| else: | |
| lang = language.lower() | |
| with st.spinner("Matching tenders..."): | |
| t0 = time.time() | |
| matches = RANKER.rank(profile, top_k=int(top_k)) | |
| elapsed = time.time() - t0 | |
| label = profile.get("name", "Your Organisation") | |
| if lang == "fr": | |
| st.success(f"🏆 Top {top_k} Subventions pour **{label}** — Traité en {elapsed:.2f}s · {len(TENDERS)} appels analysés") | |
| else: | |
| st.success(f"🏆 Top {top_k} Tenders for **{label}** — Processed in {elapsed:.2f}s · {len(TENDERS)} tenders analysed") | |
| results_lines = [] | |
| for rank_idx, match in enumerate(matches, 1): | |
| score = match["score"] | |
| breakdown = match["breakdown"] | |
| budget_str = format_budget(match.get("budget", 0)) | |
| lang_badge = "🇫🇷 FR" if match["language"] == "fr" else "🇬🇧 EN" | |
| disq = get_top_disqualifier(profile, match) | |
| if use_llm and GROQ_AVAILABLE: | |
| with st.spinner(f"⚡ Groq generating explanation for match #{rank_idx}..."): | |
| summary = groq_explain_match(profile, match, rank_idx, score, breakdown, lang) | |
| else: | |
| summary = generate_summary(profile, match, rank_idx, score, breakdown, lang) | |
| with st.container(): | |
| st.markdown(f"### #{rank_idx} — {match['title']}") | |
| m1, m2, m3, m4 = st.columns(4) | |
| m1.metric("Score", f"{score:.4f}") | |
| m2.metric("Sector", match["sector"]) | |
| m3.metric("Budget", budget_str) | |
| m4.metric("Language", lang_badge) | |
| st.markdown( | |
| f"**ID:** `{match['tender_id']}` | " | |
| f"**Deadline:** {match['deadline']} | " | |
| f"**Region:** {match['region']}" | |
| ) | |
| st.info(summary) | |
| with st.expander("📊 Score Breakdown"): | |
| b1, b2, b3, b4 = st.columns(4) | |
| b1.metric("🔍 TF-IDF", f"{breakdown['tfidf_similarity']:.3f}") | |
| b2.metric("🏷 Sector", f"{breakdown['sector_match']:.3f}") | |
| b3.metric("💰 Budget", f"{breakdown['budget_score']:.3f}") | |
| b4.metric("⏰ Urgency", f"{breakdown['urgency_score']:.3f}") | |
| st.warning(f"⚠ Biggest Disqualifier: {disq}") | |
| st.divider() | |
| results_lines.append(f"### #{rank_idx} — {match['title']}") | |
| results_lines.append(f"**Score:** {score:.4f} | **Sector:** {match['sector']} | **Budget:** {budget_str} | {lang_badge}") | |
| results_lines.append(f"**Deadline:** {match['deadline']} | **Region:** {match['region']}") | |
| results_lines.append(f"\n> {summary}\n") | |
| results_lines.append(f"**Biggest Disqualifier:** {disq}\n---") | |
| results_md = "\n".join(results_lines) | |
| ensure_dir("summaries") | |
| profile_id = profile.get("id", "custom") | |
| summary_path = f"summaries/profile_{profile_id}_{lang}.md" | |
| with open(summary_path, "w", encoding="utf-8") as f: | |
| f.write(results_md) | |
| scores_data = { | |
| "profile_id": profile_id, | |
| "profile_name": profile.get("name"), | |
| "language": lang, | |
| "llm_used": use_llm and GROQ_AVAILABLE, | |
| "elapsed_seconds": round(elapsed, 3), | |
| "matches": [ | |
| { | |
| "rank": i + 1, | |
| "tender_id": m["tender_id"], | |
| "title": m["title"], | |
| "score": m["score"], | |
| "breakdown": m["breakdown"], | |
| } | |
| for i, m in enumerate(matches) | |
| ], | |
| } | |
| scores_json = json.dumps(scores_data, indent=2) | |
| plain_summary = f"Results for {profile.get('name', 'your organisation')}. " | |
| for i, m in enumerate(matches, 1): | |
| plain_summary += f"Number {i}: {m['title']}, score {m['score']:.2f}. " | |
| st.subheader("📥 Export Results") | |
| tab1, tab2, tab3 = st.tabs(["📄 Markdown Preview", "📊 JSON Scores", "🔊 Plain Text (Audio/WhatsApp)"]) | |
| with tab1: | |
| st.download_button( | |
| "⬇ Download Markdown", | |
| data=results_md, | |
| file_name=f"matches_{profile_id}_{lang}.md", | |
| mime="text/markdown", | |
| ) | |
| st.divider() | |
| # Build a richer markdown document and render it properly | |
| header_md = ( | |
| f"## 🌍 Tender Match Report\n" | |
| f"**Organisation:** {profile.get('name', '—')} | " | |
| f"**Sector:** {profile.get('sector', '—')} | " | |
| f"**Country:** {profile.get('country', '—')}\n\n" | |
| f"**Language:** {lang.upper()} | " | |
| f"**LLM Explanations:** {'✅ Yes' if use_llm and GROQ_AVAILABLE else '❌ No'} | " | |
| f"**Processing time:** {elapsed:.2f}s\n\n" | |
| f"---\n" | |
| ) | |
| st.markdown(header_md) | |
| for rank_idx, match in enumerate(matches, 1): | |
| score = match["score"] | |
| breakdown = match["breakdown"] | |
| budget_str = format_budget(match.get("budget", 0)) | |
| lang_badge = "🇫🇷 FR" if match["language"] == "fr" else "🇬🇧 EN" | |
| disq = get_top_disqualifier(profile, match) | |
| # Retrieve already-generated summary from results_lines | |
| summary_text = "" | |
| for line in results_lines: | |
| if line.startswith(f"> ") and results_lines.index(line) > 0: | |
| prev = results_lines[results_lines.index(line) - 2] | |
| if f"#{rank_idx} —" in prev: | |
| summary_text = line[2:].strip() | |
| break | |
| score_pct = int(score * 100) | |
| bar_filled = "█" * (score_pct // 5) | |
| bar_empty = "░" * (20 - score_pct // 5) | |
| match_md = ( | |
| f"### #{rank_idx} — {match['title']}\n\n" | |
| f"| Field | Value |\n" | |
| f"|---|---|\n" | |
| f"| 🏷 Sector | `{match['sector']}` |\n" | |
| f"| 💰 Budget | {budget_str} |\n" | |
| f"| 📅 Deadline | {match['deadline']} |\n" | |
| f"| 🌍 Region | {match['region']} |\n" | |
| f"| 🗣 Language | {lang_badge} |\n" | |
| f"| 🎯 Score | **{score:.4f}** — `{bar_filled}{bar_empty}` |\n\n" | |
| f"**Score Breakdown:** " | |
| f"TF-IDF `{breakdown['tfidf_similarity']:.3f}` · " | |
| f"Sector `{breakdown['sector_match']:.3f}` · " | |
| f"Budget `{breakdown['budget_score']:.3f}` · " | |
| f"Urgency `{breakdown['urgency_score']:.3f}`\n\n" | |
| f"> ⚠️ **Biggest disqualifier:** {disq}\n\n" | |
| ) | |
| st.markdown(match_md) | |
| if summary_text: | |
| st.info(summary_text) | |
| st.divider() | |
| with tab2: | |
| st.download_button( | |
| "⬇ Download JSON", | |
| data=scores_json, | |
| file_name=f"scores_{profile_id}_{lang}.json", | |
| mime="application/json", | |
| ) | |
| st.code(scores_json, language="json") | |
| with tab3: | |
| st.text_area( | |
| "Audio-friendly summary (copy for WhatsApp/SMS)", | |
| plain_summary, | |
| height=120, | |
| ) | |