first commit

2026-07-09 19:44:20 +00:00 · 2026-01-09 07:37:56 -03:00
parent f28a9f79f4
commit 8483d9b8e9
4 changed files with 250 additions and 0 deletions
--- a/files/app.py
+++ b/files/app.py
@@ -0,0 +1,67 @@
+from flask import Flask, render_template, request, jsonify
+import traceback
+import json
+
+# 🔥 IMPORTA SEU PIPELINE
+from oci_genai_llm_graphrag_financial import answer_question
+
+app = Flask(__name__)
+
+def parse_llm_json(raw: str) -> dict:
+    try:
+        raw = raw.replace("```json", "")
+        raw = raw.replace("```", "")
+        return json.loads(raw)
+    except Exception:
+        return {
+            "answer": "ERROR",
+            "justification": "LLM returned invalid JSON",
+            "raw_output": raw
+        }
+
+# =========================
+# Health check (Load Balancer)
+# =========================
+@app.route("/health", methods=["GET"])
+def health():
+    return jsonify({"status": "UP"}), 200
+
+
+# =========================
+# Página Web
+# =========================
+@app.route("/", methods=["GET"])
+def index():
+    return render_template("index.html")
+
+
+# =========================
+# Endpoint de Chat
+# =========================
+@app.route("/chat", methods=["POST"])
+def chat():
+    try:
+        data = request.get_json()
+        question = data.get("question", "").strip()
+
+        if not question:
+            return jsonify({"error": "Empty question"}), 400
+
+        raw_answer = answer_question(question)
+        parsed_answer = parse_llm_json(raw_answer)
+
+        return jsonify({
+            "question": question,
+            "result": parsed_answer
+        })
+
+    except Exception as e:
+        traceback.print_exc()
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(
+        host="0.0.0.0",
+        port=8100,
+        debug=False
+    )
--- a/files/index.html
+++ b/files/index.html
@@ -0,0 +1,98 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>GraphRAG Chat</title>
+    <style>
+        body {
+            background: linear-gradient(to bottom right, #0f172a, #1e293b);
+            min-height: 100vh;
+            color: #e2e8f0;
+        }
+        pre { white-space: pre-wrap; }
+    </style>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            background: #0f172a;
+            color: #e5e7eb;
+            padding: 30px;
+        }
+        textarea {
+            width: 100%;
+            height: 80px;
+            font-size: 16px;
+        }
+        button {
+            margin-top: 10px;
+            padding: 10px 20px;
+            font-size: 16px;
+        }
+        pre {
+            background: #020617;
+            padding: 20px;
+            white-space: pre-wrap;
+            border-radius: 8px;
+        }
+    </style>
+</head>
+<body>
+
+<h1>🧠 Oracle GraphRAG Chat</h1>
+
+<div class="mt-12 mb-8 text-center">
+    <div class="inline-block bg-slate-800/40 px-6 py-4 rounded-2xl shadow-lg border border-slate-700">
+
+        <p class="text-slate-400 text-sm">
+            Oracle LAD A-Team<br/>
+            <span class="text-blue-300 font-semibold">Cristiano Hoshikawa</span><br/>
+            <span class="text-blue-300 font-semibold">cristiano.hoshikawa@oracle.com</span>
+        </p>
+
+        <p class="text-slate-500 text-xs mt-1 italic">
+            <span class="text-blue-300 font-semibold">Tutorial in: https://docs.oracle.com/en/learn/oci-genai-pdf</span><br/>
+        </p>
+
+        <p class="text-slate-500 text-xs mt-1 italic">
+            GraphRAG • Oracle 23ai • Embeddings • LLM • Flask API
+        </p>
+
+        <div class="mt-2 text-slate-500 text-xs flex items-center justify-center gap-1">
+            <span>Demo Version</span>
+        </div>
+
+    </div>
+</div>
+
+<textarea id="question" placeholder="Ask a question..."></textarea>
+<br>
+<button onclick="send()">Ask</button>
+
+<h2>Answer</h2>
+<pre id="answer"></pre>
+
+<script>
+    async function send() {
+        const question = document.getElementById("question").value;
+        const answerBox = document.getElementById("answer");
+
+        answerBox.textContent = "⏳ Thinking...";
+
+        const res = await fetch("/chat", {
+            method: "POST",
+            headers: {"Content-Type": "application/json"},
+            body: JSON.stringify({question})
+        });
+
+        const data = await res.json();
+
+        if (data.result && data.result.answer) {
+            answerBox.textContent = JSON.stringify(data.result, null, 2);
+        } else {
+            answerBox.textContent = "❌ Error: " + JSON.stringify(data);
+        }
+    }
+</script>
+
+</body>
+</html>