update guis 12

app.py

@@ -7,51 +7,57 @@ from langchain_huggingface.embeddings import HuggingFaceEmbeddings
 from langchain_huggingface.llms import HuggingFacePipeline
 from langchain.chains import RetrievalQA
 
-def load_vectorstore():
-    docs, files = [], []
-    for fn in os.listdir("document"):
-        if fn.lower().endswith(".pdf"):
-            docs.extend(PyPDFLoader(os.path.join("document", fn)).load_and_split())
-            files.append(fn)
-    emb = HuggingFaceEmbeddings(model_name="KBLab/sentence-bert-swedish-cased")
-    vs = FAISS.from_documents(docs, emb)
-    return vs, files
-
-# Bygg index + modell
-vectorstore, file_list = load_vectorstore()
+# ─── 1) Ladda och indexera PDF:er ──────────────────────────────
+docs, files = [], []
+for fn in os.listdir("document"):
+    if fn.lower().endswith(".pdf"):
+        loader = PyPDFLoader(os.path.join("document", fn))
+        docs.extend(loader.load_and_split())
+        files.append(fn)
+emb = HuggingFaceEmbeddings(model_name="KBLab/sentence-bert-swedish-cased")
+vs = FAISS.from_documents(docs, emb)
+
+# ─── 2) Initiera LLM & RAG-kedja ──────────────────────────────
 pipe = pipeline("text-generation", model="tiiuae/falcon-rw-1b", device=-1)
-llm = HuggingFacePipeline(pipeline=pipe, model_kwargs={"temperature":0.3,"max_new_tokens":512})
-qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever())
-
-# Gradio-UI
+llm = HuggingFacePipeline(
+    pipeline=pipe,
+    model_kwargs={"temperature": 0.3, "max_new_tokens": 512}
+)
+qa = RetrievalQA.from_chain_type(llm=llm, retriever=vs.as_retriever())
+
+# ─── 3) Chat-funktion ──────────────────────────────────────────
+def chat_fn(message, temp, history):
+    history = history or []
+    # 3a) Lång fråga?
+    if len(message) > 1000:
+        history.append((message, f"⚠️ Din fråga är för lång ({len(message)} tecken)."))
+        return history
+    # 3b) Justera temperatur och hämta svar
+    llm.model_kwargs["temperature"] = temp
+    try:
+        answer = qa.invoke({"query": message})["result"]
+    except Exception as e:
+        answer = f"Ett fel uppstod: {e}"
+    history.append((message, answer))
+    return history
+
+# ─── 4) Bygg Gradio‐UI ─────────────────────────────────────────
 with gr.Blocks() as demo:
-    status = gr.Markdown("🔄 Laddar…", elem_id="status")
-    status.visible = False
-
-    gr.Markdown("**✅ Klart!** Fråga om PDF-filerna:\n\n" + "\n".join(f"- {f}" for f in file_list))
+    gr.Markdown("## 🌟 Dokumentassistent (Svenska)")
+    gr.Markdown(
+        "**✅ Laddade PDF-filer:**\n\n" +
+        "\n".join(f"- {f}" for f in files)
+    )
 
     with gr.Row():
-        txt = gr.Textbox(label="Din fråga:")
-        temp = gr.Slider(0,1,value=0.3,step=0.05,label="Temperatur")
-        send = gr.Button("Skicka")
+        txt   = gr.Textbox(label="Din fråga:", placeholder="Ex: Vad handlar dokumentet om?")
+        temp  = gr.Slider(0.0, 1.0, value=0.3, step=0.05, label="Temperatur")
+        send  = gr.Button("Skicka")
 
     chatbot = gr.Chatbot()
 
-    def chat_fn(message, temperature, history):
-        if not message:
-            return history
-        if len(message)>1000:
-            reply = f"⚠️ Frågan är för lång ({len(message)} tecken)."
-        else:
-            llm.model_kwargs["temperature"] = temperature
-            try:
-                reply = qa.invoke({"query":message})["result"]
-            except Exception as e:
-                reply = f"Ett fel uppstod: {e}"
-        history = history or []
-        history.append((message, reply))
-        return history
-
-    send.click(fn=chat_fn, inputs=[txt, temp, chatbot], outputs=chatbot)
+    send.click(fn=chat_fn,
+               inputs=[txt, temp, chatbot],
+               outputs=chatbot)
 
 demo.launch()