Update app.py

app.py

@@ -1,24 +1,12 @@
+import os
 import time
-import tiktoken
 import streamlit as st
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings.base import Embeddings
-from langchain.vectorstores import FAISS
-from langchain.indexes import VectorstoreIndexCreator
-from langchain.chains import RetrievalQA
-from langchain.chat_models import ChatOpenAI
-from typing import List
-from together import Together
+from groq import Groq
 
+# ----------------- تنظیمات صفحه -----------------
+st.set_page_config(page_title="چت‌بات ارتش - Powered by Groq", page_icon="🪖", layout="wide")
 
-# from langchain.embeddings import TogetherEmbeddings
-from langchain.schema import Document as LangchainDocument
-
-
-
-st.set_page_config(page_title="چت‌ بات ارتش", page_icon="🪖", layout="wide")
-
+# استایل فارسی و بک‌گراند
 st.markdown("""
     <style>
     @import url('https://fonts.googleapis.com/css2?family=Vazirmatn:wght@400;700&display=swap');
@@ -86,108 +74,38 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
+# ----------------- لوگو و عنوان -----------------
 col1, col2, col3 = st.columns([1, 1, 1])
 with col2:
     st.image("army.png", width=240)
 
 st.markdown("""
     <div class="header-text">
-        <h1>چت‌ بات ارتش</h1>
-        <div class="subtitle">دستیار هوشمند برای تصمیم‌گیری در میدان نبرد</div>
+        <h1>چت‌بات ارتش</h1>
+        <div class="subtitle">دستیار هوشمند میدان نبرد - Powered by Groq</div>
     </div>
 """, unsafe_allow_html=True)
 
+# ----------------- اتصال به Groq -----------------
+api_key = "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"
 
-class TogetherEmbeddings(Embeddings):
-    def __init__(self, model_name: str, api_key: str):
-        self.model_name = model_name
-        self.client = Together(api_key=api_key)
-
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        response = self.client.embeddings.create(model=self.model_name, input=texts)
-        return [item.embedding for item in response.data]
-
-    def embed_query(self, text: str) -> List[float]:
-        return self.embed_documents([text])[0]
-
-        
-def count_tokens(text, model_name="gpt-3.5-turbo"):
-    enc = tiktoken.encoding_for_model(model_name)
-    return len(enc.encode(text))
-    
-@st.cache_resource
-def get_pdf_index():
-    with st.spinner('📄 در حال پردازش فایل PDF...'):
-        loader = [PyPDFLoader('test1.pdf')]
-        pages = []
-        for l in loader:
-            pages.extend(l.load())
-
-        splitter_initial = RecursiveCharacterTextSplitter(
-            chunk_size=124, 
-            chunk_overlap=25
-        )
-
-        small_chunks = []
-        for page in pages:
-            text = page.page_content
-            if len(text) > 124:
-                small_chunks.extend(splitter_initial.split_text(text))
-            else:
-                small_chunks.append(text)
-
-        final_chunks = []
-        max_tokens = 128
-
-        for chunk in small_chunks:
-            token_count = count_tokens(chunk, model_name="gpt-3.5-turbo")
-            if token_count > max_tokens:
-                splitter_token_safe = RecursiveCharacterTextSplitter(
-                    chunk_size=128,
-                    chunk_overlap=64
-                )
-                smaller_chunks = splitter_token_safe.split_text(chunk)
-                final_chunks.extend(smaller_chunks)
-            else:
-                final_chunks.append(chunk)
-
-        documents = [LangchainDocument(page_content=text) for text in final_chunks]
-
-        embeddings = TogetherEmbeddings(
-            model_name="togethercomputer/m2-bert-80M-32k-retrieval",
-            api_key="0291f33aee03412a47fa5d8e562e515182dcc5d9aac5a7fb5eefdd1759005979"
-        )
-
-        # اینجا دیگه Vectorstore مستقیم میسازیم با FAISS
-        vectordb = FAISS.from_documents(documents, embedding=embeddings)
-
-        return vectordb
-
-index = get_pdf_index()
-
-llm = ChatOpenAI(
-    base_url="https://api.together.xyz/v1",
-    api_key='0291f33aee03412a47fa5d8e562e515182dcc5d9aac5a7fb5eefdd1759005979',
-    model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
-)
-
-chain = RetrievalQA.from_chain_type(
-    llm=llm,
-    chain_type='stuff',
-    retriever=index.vectorstore.as_retriever(),
-    input_key='question'
-)
+client = Groq(api_key=api_key)
 
+selected_model = "llama3-70b-8192"  # بهترین مدل Groq
+
+# ----------------- استیت ذخیره‌ی پیام‌ها -----------------
 if 'messages' not in st.session_state:
     st.session_state.messages = []
 
 if 'pending_prompt' not in st.session_state:
     st.session_state.pending_prompt = None
 
+# ----------------- نمایش پیام‌های قبلی -----------------
 for msg in st.session_state.messages:
     with st.chat_message(msg['role']):
         st.markdown(f"���️ {msg['content']}", unsafe_allow_html=True)
 
+# ----------------- ورودی چت -----------------
 prompt = st.chat_input("چطور می‌تونم کمک کنم؟")
 
 if prompt:
@@ -195,17 +113,28 @@ if prompt:
     st.session_state.pending_prompt = prompt
     st.rerun()
 
+# ----------------- پاسخ دادن مدل -----------------
 if st.session_state.pending_prompt:
     with st.chat_message('ai'):
         thinking = st.empty()
         thinking.markdown("🤖 در حال فکر کردن...")
 
-        response = chain.run(f'question:پاسخ را فقط به زبان فارسی جواب بده {st.session_state.pending_prompt}')
-        answer = response.split("Helpful Answer:")[-1].strip()
-        if not answer:
-            answer = "متأسفم، اطلاعات دقیقی در این مورد ندارم."
+        try:
+            chat_completion = client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": "پاسخ را همیشه رسمی و فارسی بده."},
+                    {"role": "user", "content": st.session_state.pending_prompt}
+                ],
+                model=selected_model,
+            )
+            answer = chat_completion.choices[0].message.content.strip()
+
+        except Exception as e:
+            answer = f"خطا در پاسخ‌دهی: {str(e)}"
 
         thinking.empty()
+
+        # انیمیشن تایپ پاسخ
         full_response = ""
         placeholder = st.empty()
         for word in answer.split():