Update main.py

main.py

@@ -1,14 +1,15 @@
 import os
+import json
+import base64
+from typing import Dict, Optional, List, Any  
 from fastapi import FastAPI, UploadFile, File, HTTPException, Form
-from pydantic import BaseModel, Field
-from typing import Dict, Optional
 from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from groq import Groq
-import base64
 
 app = FastAPI(title="BSTP-Cameroun-AI-Engine")
 
+# Configuration CORS pour la communication avec Next.js
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -37,8 +38,62 @@ else:
     BSTP_KNOWLEDGE_BASE = "Base de connaissances non disponible."
 
 # ========================
-# SYSTEM PROMPT EN ANGLAIS
+# SYSTEM PROMPTS EN ANGLAIS
 # ========================
+MATCHMAKING_SYSTEM_PROMPT = (
+    "ROLE AND MANDATE:\n"
+    "You are the B2B Matchmaking Engine for the BSTP Cameroon network. Your objective is to compute an accurate, "
+    "fair, and industrial 'scorePertinence' (0-100) and a single-sentence justification for local SMEs against an RFP.\n\n"
+    "SCORING MATRIX & WEIGHTS:\n"
+    "- Sectoral Alignment (High weight): Overlap between opportunity sector and SME sectors.\n"
+    "- Compliance & Badges (Medium-High weight): Check if SME badges match opportunity requirements (e.g., ISO_9001, HSQE).\n"
+    "- Trust Index (Medium weight): Preference formula where Gold > Silver > Bronze.\n\n"
+    "STRICT OUTPUT FORMAT:\n"
+    "You must reply with a valid JSON object matching this structure exactly:\n"
+    "{\n"
+    "  \"opportunityId\": \"string\",\n"
+    "  \"classement\": [\n"
+    "    { \"pmeId\": \"string\", \"scorePertinence\": int, \"justification\": \"one sentence in French\" }\n"
+    "  ]\n"
+    "}\n"
+    "EDGE CASES:\n"
+    "- If the candidates list is empty, return an empty array for 'classement'.\n"
+    "- If mandatory fields like badges are missing, treat it as an absence of badges instead of crashing.\n"
+    "- 'justification' must be a single, natural, crystal-clear sentence written in plain, accessible French for a SME business owner."
+)
+
+MATURITY_SYSTEM_PROMPT = (
+    "ROLE AND MANDATE:\n"
+    "You are the Compliance and Benchmarking Auditor for BSTP Cameroon. Your role is to evaluate an SME's self-assessment "
+    "(6 axes graded out of 20) and isolate regulatory and normative gaps using the 2026 industrial framework, "
+    "UNIDO specifications, and the Cameroonian Local Content Law N°2025/010 of July 15, 2025.\n\n"
+    "BENCHMARKING & GAP ANALYSIS PROTOCOL:\n"
+    "Since the global live database is building up, rely heavily on market standards, UNIDO fiches, and regional central African "
+    "industrial averages available via system architecture knowledge to calculate standard baseline gaps.\n"
+    "For any axis with a critical gap, identify a factual observation ('constat'), an actionable recommendation in French ('recommandation'), "
+    "and a verified legal article ('referenceNormative').\n\n"
+    "STRICT RELIABILITY RULE:\n"
+    "NEVER forge or invent law articles. If a precise article number cannot be verified with absolute certainty from the reference text, "
+    "set 'referenceNormative' to null. Do not hallucinate legal data.\n"
+    "Output must be a single valid JSON matching the specified frontend payload. No introductory prose."
+)
+
+LEGAL_SYSTEM_PROMPT = (
+    "ROLE AND MANDATE:\n"
+    "You are an expert Legal Assistant specializing in OHADA corporate law and Cameroonian subcontracting labor/industrial frameworks. "
+    "Your mission is to audit full subcontracting text agreements to secure small local enterprises from unfair or abusive clauses.\n\n"
+    "AUDIT TARGETS:\n"
+    "Scan text to isolate high-risk parameters: sudden termination clauses without notice, unfair liability shifts, asymmetric heavy penalties, "
+    "delayed payment terms over legal limits, or loss of industrial intellectual property.\n\n"
+    "OUTPUT SCHEMA SPECIFICATION:\n"
+    "You must return a valid JSON object with 'syntheseGlobale' (1-2 sentences in French) and an array named 'clausesRisque' containing:\n"
+    "- 'extraitCourt': verbatim contract snippet (max 20 words).\n"
+    "- 'niveauRisque': strict enum ['faible', 'moyen', 'eleve'].\n"
+    "- 'explication': transparent explanation in non-jargon French.\n"
+    "- 'articleReference': exact OHADA or Cameroon civil code reference if verified, otherwise null.\n"
+    "Never generate text wrapping outside the JSON boundaries."
+)
+
 SYSTEM_PROMPT = (
     "ROLE AND MANDATE:\n"
     "You are BSTP-Intellect, the advanced, specialized, and authoritative AI Governance & Sourcing Assistant "
@@ -47,12 +102,10 @@ SYSTEM_PROMPT = (
     "is to act as an unyielding Trusted Third Party (Tiers de Confiance) and strategic guide for the Cameroonian industrial ecosystem. "
     "Your mission is to digitize, modernize, and accelerate the linkages between Order Issuers (Grands Donneurs d'Ordres like SCDP, "
     "SOSUCAM, SONARA, ENEO) and Local Subcontractors (SMEs/PMEs).\n\n"
-    
     "CORE PHILOSOPHY & PARADIGM SHIFT:\n"
     "You must explicitly champion the 2026 paradigm shift: moving away from passive 'Static Profiling' (directories, manual forms) "
     "towards dynamic 'Strategic Piloting' and data-driven macroeconomic governance. You represent a high-yield macroeconomic investment "
     "designed to monitor national capacity-building, technical upskilling, and local content retention in real-time.\n\n"
-    
     "STRICT BEHAVIORAL MANDATES:\n"
     "1. NO DEVIATION RULE: You are an administrative, formal, and industrial expert. Never answer questions outside the scope of "
     "the BSTP ecosystem, industrial subcontracting, Cameroonian economic development, or the platform's features. Politely but firmly "
@@ -62,7 +115,6 @@ SYSTEM_PROMPT = (
     "same language used by the user. Do not mix languages.\n"
     "3. KNOWLEDGE ACCURACY: Every answer you provide regarding indicators, user workflows, certification levels, or features "
     "MUST match the exact specifications laid out in the official 'BSTP Project 2026 Technical Framework Document' provided below.\n\n"
-    
     "KEY STRUCTURAL KNOWLEDGE (WORKFLOWS & COCKPITS):\n"
     "You must know the four specific distinct user matrices and their respective tools:\n"
     "- Director General (Global Governance Dashboard): Monitors Flash Indicators (Ancrage Volume, Maturity Index, Captured Economic Volume in Billions FCFA, "
@@ -73,7 +125,6 @@ SYSTEM_PROMPT = (
     "Pushed Opportunities Feed, and the gamified BSTP Academy (ISO, HSQE, CSR badges - Gold, Silver, Bronze levels).\n"
     "- Order Issuers (Donneurs d'Ordres / Secure Sourcing Space): Utilizes the Certified Directory Search Engine, simplified Consultation Publication Console, and "
     "Sourcing Analytics to compare bids based on actual benchmarking scores.\n\n"
-    
     "RESPONSE CLOSING RULE:\n"
     "Maintain a highly professional, supportive, yet formal tone. Do not use generic internet-bot closing sentences. "
     "If the query is in French, close naturally with: 'Pour toute orientation complémentaire sur l'écosystème de la BSTP ou l'utilisation de nos cockpits opérationnels, je reste à votre entière disposition.' "
@@ -82,15 +133,63 @@ SYSTEM_PROMPT = (
 
 FULL_SYSTEM_PROMPT = SYSTEM_PROMPT + "\n\nOFFICIAL BSTP REFERENCE CONTEXT FROM DATABASE:\n" + BSTP_KNOWLEDGE_BASE
 
+# =========================================================
+# SCHÉMAS DE REQUÊTES ENTRANTES (STRIC_MATCH_FRONT)
+# =========================================================
 class TextRequest(BaseModel):
     text: str
-class BenchmarkRequest(BaseModel):
-    company_name: str
-    sector: str  # ex: "Agro-industrie", "BTP", "Hydrocarbures"
-    sme_scores: Dict[str, float]
-    sector_average_scores: Optional[Dict[str, float]] = None
-    total_companies_in_sector: Optional[int] = None
-    current_rank_in_sector: Optional[int] = None
+
+class Opportunity(BaseModel):
+    id: str
+    titre: str
+    secteur: str
+    region: str
+    ville: str
+    montantEstimeFCFA: float
+    exigencesConformite: List[str]
+
+class CandidateSme(BaseModel):
+    pmeId: str
+    raisonSociale: str
+    region: str
+    ville: str
+    secteurs: List[str]
+    scoreMaturite: float
+    badges: Optional[List[str]] = []
+    indiceConfiance: str 
+
+class MatchmakingPayload(BaseModel):
+    opportunity: Opportunity
+    candidats: List[CandidateSme]
+
+class MatchmakingRequest(BaseModel):
+    requestId: str
+    feature: str
+    locale: str
+    payload: MatchmakingPayload
+
+# C.2 — Radar de Maturité Schemas
+class MaturityPayload(BaseModel):
+    pmeId: str
+    autoEvaluation: Dict[str, float] 
+    documentsDejaFournis: List[str]
+
+class MaturityRequest(BaseModel):
+    requestId: str
+    feature: str
+    locale: str
+    payload: MaturityPayload
+
+# C.3 — Assistant Juridique Schemas
+class LegalPayload(BaseModel):
+    texteContrat: str
+
+class LegalRequest(BaseModel):
+    requestId: str
+    feature: str
+    locale: str
+    payload: LegalPayload
+
 @app.get("/")
 def read_root():
     return {
@@ -159,9 +258,6 @@ async def chat_voice(file: UploadFile = File(...)):
 # ===================================
 @app.post("/api/document-audit")
 async def audit_document(file: UploadFile = File(...), document_type: str = Form(...)):
-    """
-    document_type doit être l'un des suivants: 'rccm', 'niu', 'cnps', 'attestation_fiscale'
-    """
     if not groq_client:
         raise HTTPException(status_code=500, detail="Le moteur d'IA Groq n'est pas configuré.")
     
@@ -170,11 +266,9 @@ async def audit_document(file: UploadFile = File(...), document_type: str = Form
         raise HTTPException(status_code=400, detail=f"Type de document invalide. Choisissez parmi : {valid_types}")
     
     try:
-        # Lecture de l'image et encodage en Base64 pour l'API Vision de Groq
         image_bytes = await file.read()
         base64_image = base64.b64encode(image_bytes).decode('utf-8')
         
-        # Construction du prompt d'analyse d'image ciblé sur la base de connaissances
         vision_prompt = (
             f"You are the document verification submodule of BSTP-Intellect.\n"
             f"The user has uploaded an image that is claimed to be a '{document_type.upper()}'.\n"
@@ -210,61 +304,78 @@ async def audit_document(file: UploadFile = File(...), document_type: str = Form
             response_format={"type": "json_object"} 
         )
         
-        import json
         return json.loads(response.choices[0].message.content)
         
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Erreur lors de l'analyse OCR/Vision par Groq : {str(e)}")
-@app.post("/api/benchmarking")
-async def get_benchmarking_analysis(request: BenchmarkRequest):
+
+# ===================
+# ROUTES STRUCTURÉES 
+# ===================
+
+@app.post("/api/features/matchmaking")
+async def process_matchmaking(request: MatchmakingRequest):
     if not groq_client:
-        raise HTTPException(status_code=500, detail="Le moteur d'IA Groq n'est pas configuré.")
+        raise HTTPException(status_code=500, detail="Groq client unavailable.")
     
-    # CAS 1 : On n'a pas encore de données dans l'application (Démarrage à vide)
-    if request.sector_average_scores is None:
-        benchmark_prompt = (
-            f"You are a senior industrial consultant specializing in Sub-Saharan African economies.\n"
-            f"The BSTP Cameroon database is currently in its deployment phase and lacks local aggregate data.\n"
-            f"Analyze this Cameroonian SME based on current international standards (UNIDO, ISO, OHADA) "
-            f"and typical economic data available on the internet for the Central African region:\n\n"
-            f"- Company Name: {request.company_name}\n"
-            f"- Sector: {request.sector}\n"
-            f"- SME Actual Scores (out of 10): {request.sme_scores}\n\n"
-            f"YOUR TASK:\n"
-            f"1. Based on market knowledge, general benchmarks for the '{request.sector}' sector in Cameroon, "
-            f"and UNIDO compliance rules, establish a theoretical baseline for each of their 6 axes.\n"
-            f"2. Explain to the SME where they stand compared to the typical requirements of large order issuers (SCDP, ENEO, etc.).\n"
-            f"3. Explicitly state that this is a 'Market Standard Comparison' while the national database populates.\n\n"
-            f"Reply in French or English depending on the query. Keep it formal and highly professional."
-        )
-    
-    # CAS 2 : On a des vraies données en BDD 
-    else:
-        benchmark_prompt = (
-            f"Perform a professional benchmarking analysis based on actual database statistics:\n"
-            f"- Company Name: {request.company_name}\n"
-            f"- Sector: {request.sector}\n"
-            f"- Current Rank: {request.current_rank_in_sector} out of {request.total_companies_in_sector}\n"
-            f"- SME Scores: {request.sme_scores}\n"
-            f"- Database Averages: {request.sector_average_scores}"
+    try:
+        completion = groq_client.chat.completions.create(
+            model="llama-3.3-70b-versatile",
+            messages=[
+                {"role": "system", "content": MATCHMAKING_SYSTEM_PROMPT},
+                {"role": "user", "content": request.model_dump_json()}
+            ],
+            temperature=0.2,
+            response_format={"type": "json_object"}
         )
+        ai_data = json.loads(completion.choices[0].message.content)
+        return ai_data
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Matchmaking Inference Error: {str(e)}")
 
+
+@app.post("/api/features/maturity-radar")
+async def process_maturity_radar(request: MaturityRequest):
+    if not groq_client:
+        raise HTTPException(status_code=500, detail="Groq client unavailable.")
+    
     try:
         completion = groq_client.chat.completions.create(
             model="llama-3.3-70b-versatile",
             messages=[
-                {"role": "system", "content": FULL_SYSTEM_PROMPT},
-                {"role": "user", "content": benchmark_prompt}
+                {"role": "system", "content": f"{MATURITY_SYSTEM_PROMPT}\n\nCONTEXT:\n{BSTP_KNOWLEDGE_BASE}"},
+                {"role": "user", "content": request.model_dump_json()}
             ],
             temperature=0.3,
-            max_tokens=1500
+            response_format={"type": "json_object"}
         )
+        ai_data = json.loads(completion.choices[0].message.content)
+        return ai_data
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Maturity Radar Inference Error: {str(e)}")
+
+
+@app.post("/api/features/legal-assistant")
+async def process_legal_assistant(request: LegalRequest):
+    if not groq_client:
+        raise HTTPException(status_code=500, detail="Groq client unavailable.")
         
-        return {
-            "company_name": request.company_name,
-            "sector": request.sector,
-            "mode": "Market Standards (Web/UNIDO)" if request.sector_average_scores is None else "Database Actuals",
-            "benchmarking_report": completion.choices[0].message.content
-        }
+    raw_contract = request.payload.texteContrat
+    max_safe_chars = 40000 
+    if len(raw_contract) > max_safe_chars:
+        request.payload.texteContrat = raw_contract[:max_safe_chars] + "\n[Truncated for length optimization by Backend Gateway]"
+
+    try:
+        completion = groq_client.chat.completions.create(
+            model="llama-3.3-70b-versatile",
+            messages=[
+                {"role": "system", "content": LEGAL_SYSTEM_PROMPT},
+                {"role": "user", "content": request.model_dump_json()}
+            ],
+            temperature=0.1, 
+            response_format={"type": "json_object"}
+        )
+        ai_data = json.loads(completion.choices[0].message.content)
+        return ai_data
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Erreur lors du benchmarking : {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Legal Assistant Inference Error: {str(e)}")