Update app.py

app.py

@@ -1,102 +1,54 @@
 import gradio as gr
-import json
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.document_loaders import WebBaseLoader
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain_huggingface import HuggingFaceEndpoint
+import requests
+import os
 
-# -----------------------------
-# 1️⃣ Load pipeline JSON (Flowise export)
-# -----------------------------
-PIPELINE_PATH = "Chat1 Chatflow.json"
-with open(PIPELINE_PATH, "r") as f:
-    pipeline = json.load(f)
+# --- Flowise endpoint ---
+# Nếu bạn chạy Flowise cục bộ: ngrok http 3000
+FLOWISE_API = os.getenv("FLOWISE_API", "https://<your-ngrok-or-render-url>/api/v1/prediction")
 
-# Lấy URL trong pipeline (nếu có)
-web_url = None
-for node in pipeline.get("nodes", []):
-    if "url" in node.get("data", {}):
-        web_url = node["data"]["url"]
-
-if not web_url:
-    web_url = "https://recht.nrw.de/lmi/owa/br_text_anzeigen?v_id=10000000000000000654"
-
-# -----------------------------
-# 2️⃣ Load documents (from website)
-# -----------------------------
-print(f"🌐 Lade Text von: {web_url}")
-loader = WebBaseLoader(web_url)
-docs = loader.load()
-
-splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
-docs_split = splitter.split_documents(docs)
-
-# -----------------------------
-# 3️⃣ Embeddings + Vectorstore
-# -----------------------------
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-vectorstore = FAISS.from_documents(docs_split, embedding=embeddings)
-retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
-
-# -----------------------------
-# 4️⃣ LLM + Memory
-# -----------------------------
-llm = HuggingFaceEndpoint(
-    repo_id="mistralai/Mistral-7B-Instruct-v0.2",
-    temperature=0.0,
-    huggingfacehub_api_token=None  # Hugging Face Space đã tự có quyền
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
-# -----------------------------
-# 5️⃣ RAG Chain
-# -----------------------------
-qa_chain = ConversationalRetrievalChain.from_llm(
-    llm=llm,
-    retriever=retriever,
-    memory=memory,
-    return_source_documents=True
-)
-
-# -----------------------------
-# 6️⃣ Chat function
-# -----------------------------
 def chat_with_bot(message, history):
-    result = qa_chain({"question": message})
-    answer = result.get("answer", "Keine Antwort gefunden.")
-    sources = result.get("source_documents", [])
-    if sources:
-        refs = "\n\n**Quellen:**\n" + "\n".join([
-            f"- {src.metadata.get('source', 'Unbekannt')}"
-            for src in sources
-        ])
-    else:
-        refs = ""
-    return answer + refs
+    """
+    Gửi câu hỏi người dùng đến Flowise API và nhận câu trả lời.
+    """
+    payload = {"question": message}
+    try:
+        response = requests.post(FLOWISE_API, json=payload, timeout=60)
+        data = response.json()
+
+        answer = data.get("text", "Keine Antwort gefunden.")
+        sources = data.get("sourceDocuments", [])
+
+        if sources:
+            refs = "\n\n**Quellen:**\n" + "\n".join([
+                f"- {src.get('metadata', {}).get('source', 'Unbekannt')}"
+                for src in sources
+            ])
+        else:
+            refs = ""
+
+        return answer + refs
+
+    except Exception as e:
+        return f"Fehler: {e}"
 
-# -----------------------------
-# 7️⃣ UI
-# -----------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 🤖 Chatbot für Prüfungsrecht")
-    gr.Markdown(
-        "Fragen Sie zur Prüfungsordnung oder zum Hochschulgesetz NRW. "
-        "Der Chatbot antwortet auf Basis der aus Flowise exportierten Pipeline "
-        "und zitiert direkt aus der Quelle (recht.nrw.de)."
-    )
+    gr.Markdown("# 🤖 Chatbot für Prüfungsrecht")
+    gr.Markdown("Fragen Sie zur Prüfungsordnung oder zum Hochschulgesetz NRW. "
+                "Der Chatbot zitiert direkt aus den Quellen (PDF & Webseite).")
 
     chat = gr.ChatInterface(
         fn=chat_with_bot,
-        title="Prüfungsrecht RAG-Chatbot (Serverless)",
+        title="Prüfungsrecht RAG-Chatbot",
         examples=[
             "Was steht in §10 über Wiederholungsprüfungen?",
-            "Welche Regel gilt laut Hochschulgesetz für Prüfungsanspruch?"
+            "Welche Regel gilt laut Hochschulgesetz für Prüfungsanspruch?",
         ]
     )
 
+    gr.HTML("""
+    <h3>📄 Quellen anzeigen</h3>
+    <iframe src="https://recht.nrw.de/lmi/owa/br_text_anzeigen?v_id=10000000000000000654" width="100%" height="400"></iframe>
+    """)
+
 if __name__ == "__main__":
     demo.launch()