update guis 11

app.py

@@ -1,78 +1,57 @@
-import gradio as gr
 import os
-
+import gradio as gr
+from transformers import pipeline
 from langchain_community.document_loaders import PyPDFLoader
 from langchain_community.vectorstores import FAISS
 from langchain_huggingface.embeddings import HuggingFaceEmbeddings
 from langchain_huggingface.llms import HuggingFacePipeline
 from langchain.chains import RetrievalQA
-from transformers import pipeline
 
-# 1) Ladda och indexera PDF:er
 def load_vectorstore():
-    all_docs, files = [], []
+    docs, files = [], []
     for fn in os.listdir("document"):
         if fn.lower().endswith(".pdf"):
-            path = os.path.join("document", fn)
-            docs = PyPDFLoader(path).load_and_split()
-            all_docs.extend(docs)
+            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(all_docs, emb)
+    vs = FAISS.from_documents(docs, emb)
     return vs, files
 
-# 2) Skapa Blocks-layout
-with gr.Blocks() as demo:
-    # A) Status under uppstart
-    status = gr.Markdown("🔄 Laddar dokument och modell, vänta…", elem_id="status-text")
-    
-    # B) Indexera och initiera
-    vectorstore, loaded_files = load_vectorstore()
-    llm_pipe = pipeline("text-generation", model="tiiuae/falcon-rw-1b", device=-1)
-    llm = HuggingFacePipeline(
-        pipeline=llm_pipe,
-        model_kwargs={"temperature": 0.3, "max_new_tokens": 512}
-    )
-    qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever())
+# Bygg index + modell
+vectorstore, file_list = load_vectorstore()
+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())
 
-    # C) Dölj status och visa PDF-lista
+# Gradio-UI
+with gr.Blocks() as demo:
+    status = gr.Markdown("🔄 Laddar…", elem_id="status")
     status.visible = False
-    gr.Markdown(
-        "✅ Klar! Du kan nu ställa frågor om dokumenten nedan:\n\n" +
-        "\n".join(f"- {f}" for f in loaded_files),
-        elem_id="status-text"
-    )
 
-    # D) Temperatur-slider och text-inmatning + knapp
+    gr.Markdown("**✅ Klart!** Fråga om PDF-filerna:\n\n" + "\n".join(f"- {f}" for f in file_list))
+
     with gr.Row():
         txt = gr.Textbox(label="Din fråga:")
-        temp = gr.Slider(0, 1, value=0.3, step=0.05, label="Temperatur")
+        temp = gr.Slider(0,1,value=0.3,step=0.05,label="Temperatur")
         send = gr.Button("Skicka")
 
-    # E) Chatbot-komponent (OpenAI-stil)
-    chatbot = gr.Chatbot([], type="messages")
+    chatbot = gr.Chatbot()
 
-    # F) Logik för chatten
     def chat_fn(message, temperature, history):
-        # kortare skydd
-        if len(message) > 1000:
-            history = history or []
-            history.append(("⚠️ Din fråga är för lång, korta ner den.", ""))
+        if not message:
             return history
-        # uppdatera temp
-        llm.model_kwargs["temperature"] = temperature
-        try:
-            out = qa.invoke({"query": message})["result"]
-        except Exception as e:
-            out = f"Ett fel uppstod: {e}"
-        # bygg historia som tupler
+        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, out))
+        history.append((message, reply))
         return history
 
-    # G) Koppla knapp till chat_fn
-    send.click(fn=chat_fn,
-               inputs=[txt, temp, chatbot],
-               outputs=chatbot)
+    send.click(fn=chat_fn, inputs=[txt, temp, chatbot], outputs=chatbot)
 
 demo.launch()