Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,124 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+os.environ["STREAMLIT_WATCHER_TYPE"] = "none"
+
+import re
+import warnings
+import logging
 import streamlit as st
+from langdetect import detect
+
+from langchain_community.chat_models import ChatOpenAI
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.chains import RetrievalQA
+from langchain_core.prompts import ChatPromptTemplate
+
+# 🔐 Embed your API key directly for Streamlit Cloud deployment
+OPENAI_API_KEY = "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"
+warnings.filterwarnings("ignore")
+logging.getLogger("transformers").setLevel(logging.ERROR)
+
+st.set_page_config(page_title="RegioWizard KI", layout="centered")
+st.title('🧠 RegioWizard KI')
+
+if 'messages' not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    st.chat_message(message['role']).markdown(message['content'])
+
+def is_greeting(text):
+    return text.lower().strip() in ["hi", "hello", "hey", "greetings", "hallo", "servus", "moin"]
+
+def detect_language(text):
+    try:
+        return detect(text)
+    except:
+        return "en"
+
+@st.cache_resource
+def get_vectorstore():
+    pdf_path = "/workspaces/RegioWizard-KI/RegiowizardBot/bad_breisig_docs.pdf"  # Ensure this path is correct on Streamlit Cloud
+    loaders = [PyPDFLoader(pdf_path)]
+    return VectorstoreIndexCreator(
+        embedding=HuggingFaceEmbeddings(model_name='all-MiniLM-L12-v2'),
+        text_splitter=RecursiveCharacterTextSplitter(
+            chunk_size=600, chunk_overlap=300, separators=["\n\n", "\n", ".", "•"]
+        )
+    ).from_loaders(loaders).vectorstore
+
+def extract_political_groups(text):
+    pattern = re.compile(r'(AsF|CDU|SPD|FDP|Junge Union|Senioren-Union|Freie W[aä]hlergruppe)[^\n]*', re.IGNORECASE)
+    return '\n'.join(sorted(set([m.group(0).strip() for m in pattern.finditer(text)])))
+
+prompt = st.chat_input('Pass your prompt here')
+
+if prompt:
+    st.chat_message('user').markdown(prompt)
+    st.session_state.messages.append({'role': 'user', 'content': prompt})
+
+    try:
+        openai_chat = ChatOpenAI(
+            model_name="gpt-3.5-turbo",
+            temperature=0,
+            openai_api_key=OPENAI_API_KEY
+        )
+
+        lang = detect_language(prompt)
+
+        if is_greeting(prompt):
+            response = "Hallo, ich bin der RegioWizard_KI Chatbot! 😊 Frag mich alles über Bad Breisig!" if lang == "de" else "Hi, I'm RegioWizard_KI Chatbot! 😊 Ask me anything about Bad Breisig!"
+        else:
+            vectorstore = get_vectorstore()
+
+            qa_prompt = ChatPromptTemplate.from_template("""
+{prefix}
+
+Context:
+{context}
+
+{q_prefix}: {question}
+
+{a_prefix}:
+""").partial(
+                prefix="Du bist ein hilfsbereiter Assistent mit Wissen über Bad Breisig. Verwende AUSSCHLIESSLICH den untenstehenden Kontext, um die Frage des Nutzers zu beantworten." if lang == "de" else "You are a helpful assistant knowledgeable about Bad Breisig. Use ONLY the context below to answer the user's question.",
+                q_prefix="Frage" if lang == "de" else "Question",
+                a_prefix="Antwort" if lang == "de" else "Answer"
+            )
+
+            chain = RetrievalQA.from_chain_type(
+                llm=openai_chat,
+                chain_type='stuff',
+                retriever=vectorstore.as_retriever(search_kwargs={'k': 12}),
+                chain_type_kwargs={"prompt": qa_prompt},
+                return_source_documents=True
+            )
+
+            result = chain({"query": prompt})
+            response = result["result"].strip()
+
+            if any(x in prompt.lower() for x in ["partei", "gruppierung", "gruppen", "parties", "political"]):
+                fallback_docs = result.get("source_documents", [])
+                combined_text = "\n".join(doc.page_content for doc in fallback_docs)
+                filtered = extract_political_groups(combined_text)
+                if filtered:
+                    response = f"Die politischen Gruppierungen in Bad Breisig sind:\n\n{filtered}" if lang == "de" else f"The political groups in Bad Breisig are:\n\n{filtered}"
+
+            if not response or "not found" in response.lower() or "nicht im kontext" in response.lower():
+                fallback_docs = vectorstore.similarity_search_with_score(prompt, k=3)
+                keyword_hits = list({doc.page_content.strip()[:300] for doc, _ in fallback_docs})
+
+                if keyword_hits:
+                    response = "Hier sind die relevantesten Informationen:\n\n" if lang == "de" else "Here’s the most relevant information found:\n\n"
+                    response += "\n\n".join(keyword_hits)
+                else:
+                    response = "Nicht im bereitgestellten Dokument gefunden." if lang == "de" else "Not found in the provided document."
+
+        st.chat_message('assistant').markdown(response)
+        st.session_state.messages.append({'role': 'assistant', 'content': response})
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    except Exception as e:
+        st.error(f"❌ Error: {str(e)}")