Update app.py

app.py

@@ -19,17 +19,13 @@ model = ChatGroq(api_key="gsk_kqPWbbWhDN2egNA4k8X3WGdyb3FYEaW2TzHfLhDQuzgMkTm9C7
 chat_history = []
 PRICE_PER_TOKEN = 0.00001
 
+def summarize_chat(model):
 
-def count_tokens(text):
-    return len(text.split())
-
-def calculate_price(input_text, output_text):
-    input_tokens = count_tokens(input_text)
-    output_tokens = count_tokens(output_text)
-    total_tokens = input_tokens + output_tokens
-    total_price = total_tokens * PRICE_PER_TOKEN
-    return total_tokens, f"{total_price:.6f} دلار"
-
+    chat_text = "\n".join([f"پرسش: {q}\nپاسخ: {a}" for q, a in chat_history])
+    summary_prompt = f"یک خلاصه کوتاه از مکالمه زیر ارائه کن:\n\n{chat_text}\n\nخلاصه:"
+    summary_response = model.invoke(summary_prompt)
+    return summary_response.content
+    
 def process_file(file_path):
     """Process file and store in ChromaDB."""
     if not file_path:
@@ -53,15 +49,6 @@ def process_file(file_path):
     except Exception as e:
         raise RuntimeError(f"Error processing file: {str(e)}")
 
-def remove_think_sections(response_text):
-    return re.sub(r"<think>.*?</think>", "", response_text, flags=re.DOTALL)
-
-def summarize_chat(model):
-
-    chat_text = "\n".join([f"پرسش: {q}\nپاسخ: {a}" for q, a in chat_history])
-    summary_prompt = f"یک خلاصه کوتاه از مکالمه زیر ارائه کن:\n\n{chat_text}\n\nخلاصه:"
-    summary_response = model.invoke(summary_prompt)
-    return summary_response.content
 
 def answer_query(query, file_path, summarize, tone, model_name, creativity, keywords, language, response_length, welcome_message, exclusion_words):
     global chat_history
@@ -121,18 +108,34 @@ def answer_query(query, file_path, summarize, tone, model_name, creativity, keyw
     except Exception as e:
         return f"خطا: {str(e)}", "", 0, "0 دلار"
 
-def chat_with_bot(query, file, summarize, tone, model_name, creativity, keywords, language, response_length, welcome_message, exclusion_words):
+def count_tokens(text):
+    return len(text.split())
+
+def calculate_price(input_text, output_text):
+    input_tokens = count_tokens(input_text)
+    output_tokens = count_tokens(output_text)
+    total_tokens = input_tokens + output_tokens
+    total_price = total_tokens * PRICE_PER_TOKEN
+    return total_tokens, f"{total_price:.6f} دلار"
+
+def remove_think_sections(response_text):
+    return re.sub(r"<think>.*?</think>", "", response_text, flags=re.DOTALL)
+
+def chat_with_bot(query, file, summarize, tone, model_name, creativity, keywords, language, response_length, welcome_message, exclusion_words, chat_history):
     file_path = file.name if file else None
-    return answer_query(query, file_path, summarize, tone, model_name, creativity, keywords, language, response_length, welcome_message, exclusion_words)
+    response, summary, total_tokens, price = answer_query(query, file_path, summarize, tone, model_name, creativity, keywords, language, response_length, welcome_message, exclusion_words)
 
+    chat_history.append((query, response))
+    
+    return chat_history, summary, total_tokens, price
 
 def clear_memory():
     global chat_history
     chat_history = []
-    return [], "", 0, "0 تومان"
+    return [], "", 0, "0 دلار"
 
 with gr.Blocks() as demo:
-    gr.Markdown("## 🤖 Parviz GPT")
+    gr.Markdown("## 🤖 Parviz GPT - چت بات هوش مصنوعی")
     gr.Markdown("**یک فایل (PDF یا TXT) آپلود کنید و سوال خود را بپرسید.**")
 
     chatbot = gr.Chatbot(label="💬 تاریخچه چت")