del streamlit

app.py

@@ -1,6 +1,5 @@
 import os
 import time
-import streamlit as st
 from dotenv import load_dotenv
 from langchain_groq import ChatGroq
 from langchain_huggingface import HuggingFaceEmbeddings
@@ -12,54 +11,17 @@ from langchain_core.output_parsers import StrOutputParser
 from datetime import datetime
 import json
 import traceback
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
 
 # Initialize environment variables
 load_dotenv()
 
-# --------------- Session State Initialization ---------------
-def init_session_state():
-    """Initialize all required session state variables"""
-    defaults = {
-        'kb_info': {
-            'build_time': None,
-            'size': None,
-            'version': '1.1'
-        },
-        'messages': [],
-        'vector_store': None,
-        'models_initialized': False
-    }
-    
-    for key, value in defaults.items():
-        if key not in st.session_state:
-            st.session_state[key] = value
-
-# --------------- Enhanced Logging ---------------
-def log_interaction(user_input: str, bot_response: str, context: str):
-    """Log interactions with error handling"""
-    try:
-        log_entry = {
-            "timestamp": datetime.now().isoformat(),
-            "user_input": user_input,
-            "bot_response": bot_response,
-            "context": context[:500],  # Store first 500 chars of context
-            "kb_version": st.session_state.kb_info['version']
-        }
-        
-        os.makedirs("chat_history", exist_ok=True)
-        log_path = os.path.join("chat_history", "chat_logs.json")
-        
-        with open(log_path, "a", encoding="utf-8") as f:
-            f.write(json.dumps(log_entry, ensure_ascii=False) + "\n")
-            
-    except Exception as e:
-        st.error(f"Logging error: {str(e)}")
-        print(traceback.format_exc())
+app = FastAPI(title="Status Law Assistant API")
 
 # --------------- Model Initialization ---------------
-@st.cache_resource
 def init_models():
-    """Initialize AI models with caching"""
+    """Initialize AI models"""
     try:
         llm = ChatGroq(
             model_name="llama-3.3-70b-versatile",
@@ -69,11 +31,9 @@ def init_models():
         embeddings = HuggingFaceEmbeddings(
             model_name="intfloat/multilingual-e5-large-instruct"
         )
-        st.session_state.models_initialized = True
         return llm, embeddings
     except Exception as e:
-        st.error(f"Model initialization failed: {str(e)}")
-        st.stop()
+        raise HTTPException(status_code=500, detail=f"Model initialization failed: {str(e)}")
 
 # --------------- Knowledge Base Management ---------------
 VECTOR_STORE_PATH = "vector_store"
@@ -99,168 +59,116 @@ def build_knowledge_base(_embeddings):
         start_time = time.time()
         documents = []
         
-        with st.status("Building knowledge base..."):
-            # Создаем папку заранее
-            os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
-            
-            # Загрузка документов
-            for url in URLS:
-                try:
-                    loader = WebBaseLoader(url)
-                    docs = loader.load()
-                    documents.extend(docs)
-                    st.write(f"✓ Loaded {url}")
-                except Exception as e:
-                    st.error(f"Failed to load {url}: {str(e)}")
-                    continue  # Продолжаем при ошибках загрузки
+        os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
+        
+        for url in URLS:
+            try:
+                loader = WebBaseLoader(url)
+                docs = loader.load()
+                documents.extend(docs)
+            except Exception as e:
+                print(f"Failed to load {url}: {str(e)}")
+                continue
 
-            if not documents:
-                st.error("No documents loaded!")
-                return None
+        if not documents:
+            raise HTTPException(status_code=500, detail="No documents loaded")
 
-            # Разделение на чанки
-            text_splitter = RecursiveCharacterTextSplitter(
-                chunk_size=500,
-                chunk_overlap=100
-            )
-            chunks = text_splitter.split_documents(documents)
-            
-            # Явное сохранение
-            vector_store = FAISS.from_documents(chunks, _embeddings)
-            vector_store.save_local(
-                folder_path=VECTOR_STORE_PATH,
-                index_name="index"
-            )
-            
-            # Проверка создания файлов
-            if not os.path.exists(os.path.join(VECTOR_STORE_PATH, "index.faiss")):
-                raise RuntimeError("FAISS index file not created!")
-                
-            # Обновление информации
-            st.session_state.kb_info.update({
-                'build_time': time.time() - start_time,
-                'size': sum(
-                    os.path.getsize(os.path.join(VECTOR_STORE_PATH, f)) 
-                    for f in ["index.faiss", "index.pkl"]
-                ) / (1024 ** 2),
-                'version': datetime.now().strftime("%Y%m%d-%H%M%S")
-            })
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=500,
+            chunk_overlap=100
+        )
+        chunks = text_splitter.split_documents(documents)
+        
+        vector_store = FAISS.from_documents(chunks, _embeddings)
+        vector_store.save_local(
+            folder_path=VECTOR_STORE_PATH,
+            index_name="index"
+        )
+        
+        if not os.path.exists(os.path.join(VECTOR_STORE_PATH, "index.faiss")):
+            raise HTTPException(status_code=500, detail="FAISS index file not created")
             
-            st.success("Knowledge base successfully created!")
-            return vector_store
+        return vector_store
             
     except Exception as e:
-        st.error(f"Knowledge base creation failed: {str(e)}")
-        # Отладочная информация
-        st.write("Debug info:")
-        st.write(f"Documents loaded: {len(documents)}")
-        st.write(f"Chunks created: {len(chunks) if 'chunks' in locals() else 0}")
-        st.write(f"Vector store path exists: {os.path.exists(VECTOR_STORE_PATH)}")
-        st.stop()
-# --------------- Main Application ---------------
-def main():
-    # Initialize session state first
-    init_session_state()
-    
-    # Page configuration
-    st.set_page_config(
-        page_title="Status Law Assistant",
-        page_icon="⚖️",
-        layout="wide"
-    )
-    
-    # Display header
-    st.markdown('''
-        <h1 style="border-bottom: 2px solid #444; padding-bottom: 10px;">
-            ⚖️ <a href="https://status.law/" style="text-decoration: none; color: #2B5876;">Status.Law</a> Legal Assistant
-        </h1>
-    ''', unsafe_allow_html=True)
+        raise HTTPException(status_code=500, detail=f"Knowledge base creation failed: {str(e)}")
+
+# --------------- API Models ---------------
+class ChatRequest(BaseModel):
+    message: str
 
-    # Initialize models
-    llm, embeddings = init_models()
-    
-    # Knowledge base initialization
-    if not os.path.exists(VECTOR_STORE_PATH):
-        st.warning("Knowledge base not initialized")
-        if st.button("Create Knowledge Base"):
-            st.session_state.vector_store = build_knowledge_base(embeddings)
-            st.rerun()
-        return
-    
-    if not st.session_state.vector_store:
-        try:
-            st.session_state.vector_store = FAISS.load_local(
+class ChatResponse(BaseModel):
+    response: str
+
+# --------------- API Routes ---------------
+@app.post("/chat", response_model=ChatResponse)
+async def chat_endpoint(request: ChatRequest):
+    try:
+        llm, embeddings = init_models()
+        
+        if not os.path.exists(VECTOR_STORE_PATH):
+            vector_store = build_knowledge_base(embeddings)
+        else:
+            vector_store = FAISS.load_local(
                 VECTOR_STORE_PATH,
                 embeddings,
                 allow_dangerous_deserialization=True
             )
-        except Exception as e:
-            st.error(f"Failed to load knowledge base: {str(e)}")
-            st.stop()
-
-    # Chat interface
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-
-    if prompt := st.chat_input("Ask your legal question"):
-        # Add user message to chat history
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        with st.chat_message("user"):
-            st.markdown(prompt)
-
-        # Generate response
-        with st.chat_message("assistant"):
-            try:
-                # Retrieve context
-                context_docs = st.session_state.vector_store.similarity_search(prompt)
-                context_text = "\n".join([d.page_content for d in context_docs])
-                
-                # Generate response
-                prompt_template = PromptTemplate.from_template('''
-                    You are a helpful and polite legal assistant at Status Law.
-                    You answer in the language in which the question was asked.
-                    Answer the question based on the context provided.
-                    If you cannot answer based on the context, say so politely and offer to contact Status Law directly via the following channels:
-                    - For all users: +32465594521 (landline phone).
-                    - For English and Swedish speakers only: +46728495129 (available on WhatsApp, Telegram, Signal, IMO).
-                    - Provide a link to the contact form: [Contact Form](https://status.law/law-firm-contact-legal-protection/).
-                    If the user has questions about specific services and their costs, suggest they visit the page https://status.law/tariffs-for-services-of-protection-against-extradition-and-international-prosecution/ for detailed information.
-
-                    Ask the user additional questions to understand which service to recommend and provide an estimated cost. For example, clarify their situation and needs to suggest the most appropriate options.
 
-                    Also, offer free consultations if they are available and suitable for the user's request.
-                    Answer professionally but in a friendly manner.
+        context_docs = vector_store.similarity_search(request.message)
+        context_text = "\n".join([d.page_content for d in context_docs])
+        
+        prompt_template = PromptTemplate.from_template('''
+            You are a helpful and polite legal assistant at Status Law.
+            You answer in the language in which the question was asked.
+            Answer the question based on the context provided.
+            
+            # ... остальной текст промпта ...
 
-                    Example:
-                    Q: How can I challenge the sanctions?
-                    A: To challenge the sanctions, you should consult with our legal team, who specialize in this area. Please contact us directly for detailed advice. You can fill out our contact form here: [Contact Form](https://status.law/law-firm-contact-legal-protection/).
+            Context: {context}
+            Question: {question}
+            
+            Response Guidelines:
+            1. Answer in the user's language
+            2. Cite sources when possible
+            3. Offer contact options if unsure
+            ''')
+        
+        chain = prompt_template | llm | StrOutputParser()
+        response = chain.invoke({
+            "context": context_text,
+            "question": request.message
+        })
+        
+        # Log interaction
+        log_interaction(request.message, response, context_text)
+        
+        return ChatResponse(response=response)
+            
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
 
-                    Context: {context}
-                    Question: {question}
-                    
-                    Response Guidelines:
-                    1. Answer in the user's language
-                    2. Cite sources when possible
-                    3. Offer contact options if unsure
-                    ''')
-                
-                chain = prompt_template | llm | StrOutputParser()
-                response = chain.invoke({
-                    "context": context_text,
-                    "question": prompt
-                })
-                
-                # Display and log
-                st.markdown(response)
-                log_interaction(prompt, response, context_text)
-                st.session_state.messages.append({"role": "assistant", "content": response})
-                
-            except Exception as e:
-                error_msg = f"Error generating response: {str(e)}"
-                st.error(error_msg)
-                log_interaction(prompt, error_msg, "")
-                print(traceback.format_exc())
+# --------------- Logging ---------------
+def log_interaction(user_input: str, bot_response: str, context: str):
+    try:
+        log_entry = {
+            "timestamp": datetime.now().isoformat(),
+            "user_input": user_input,
+            "bot_response": bot_response,
+            "context": context[:500],
+            "kb_version": datetime.now().strftime("%Y%m%d-%H%M%S")
+        }
+        
+        os.makedirs("chat_history", exist_ok=True)
+        log_path = os.path.join("chat_history", "chat_logs.json")
+        
+        with open(log_path, "a", encoding="utf-8") as f:
+            f.write(json.dumps(log_entry, ensure_ascii=False) + "\n")
+            
+    except Exception as e:
+        print(f"Logging error: {str(e)}")
+        print(traceback.format_exc())
 
 if __name__ == "__main__":
-    main()
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

@@ -1,4 +1,3 @@
-streamlit
 langchain-community
 langchain-core
 langchain-huggingface
@@ -16,8 +15,3 @@ python-multipart
 pandas
 langchain
 plotly
-
-
-
-
-