Update app.py

app.py

@@ -1,26 +1,19 @@
 import streamlit as st
-from huggingface_hub import InferenceClient
+from together import Together
 import os
 from typing import Iterator
 from PIL import Image
-import pytesseract
-from PyPDF2 import PdfReader
 import base64
-from together import Together
+from PyPDF2 import PdfReader
 
 API_KEY = os.getenv("TOGETHER_API_KEY")
 if not API_KEY:
     raise ValueError("API key is missing! Make sure TOGETHER_API_KEY is set in the Secrets.")
 
-
-# Initialize the client with Together AI provider
+# Initialize the Together client
 @st.cache_resource
 def get_client():
-    #return InferenceClient(
-    #    provider="together",
-    #     api_key=API_KEY
-    #)
-    return Together(api_key=API_KEY)  # Use Together.ai's official client
+    return Together(api_key=API_KEY)
 
 def process_file(file) -> str:
     """Process uploaded file and return its content"""
@@ -28,25 +21,19 @@ def process_file(file) -> str:
         return ""
 
     try:
-        # Handle PDF files
         if file.type == "application/pdf":
             text = ""
             pdf_reader = PdfReader(file)
             for page in pdf_reader.pages:
-                page_text = page.extract_text()
-                if page_text:
-                    text += page_text + "\n"
+                text += page.extract_text() + "\n"
             return text
-
-        # Handle image files
         elif file.type.startswith("image/"):
             return base64.b64encode(file.getvalue()).decode("utf-8")
-
-        # Handle text files
         else:
             return file.getvalue().decode('utf-8')
     except Exception as e:
-        return f"Error processing file: {str(e)}"
+        st.error(f"파일 처리 중 오류 발생: {str(e)}")
+        return ""
 
 def generate_response(
     message: str,
@@ -58,63 +45,31 @@ def generate_response(
     files=None
 ) -> Iterator[str]:
     client = get_client()
-
-    has_images = False
-    content_blocks = []
-    image_content = None  # To store image data
-    image_mime_type = None  # To store MIME type
-
-    if files:
-        for file in files:
-            content = process_file(file)
-            if file.type.startswith("image/"):
-                has_images = True
-                image_content = content  # Already base64 encoded
-                image_mime_type = file.type  # Store MIME type
-            else:
-                content_blocks.append({
-                    "type": "text",
-                    "text": f"File content:\n{content}"
-                })
-
-    # Build messages
-    messages = [{"role": "system", "content": system_message}]
-
-    # Add history
-    for user_msg, assistant_msg in history:
-        messages.append({"role": "user", "content": user_msg})
-        messages.append({"role": "assistant", "content": assistant_msg})
-
+    
     try:
-        if has_images:
-            # Vision model request
-            vision_messages = [{
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": message},
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:{image_mime_type};base64,{image_content}",
-                        },
-                    },
-                ]
-            }]
-
-            stream = client.chat.completions.create(
-                model="meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",                
-                messages=vision_messages,
-                stream=True,
-            )
-
-        else:
-            # Text-only model request
-            current_message = {
-                "role": "user",
-                "content": [{"type": "text", "text": message}] + content_blocks
-            }
-            messages.append(current_message)
-
+        # 메시지 형식 수정
+        messages = [{"role": "system", "content": system_message}]
+        
+        # 히스토리 추가
+        for user_msg, assistant_msg in history:
+            messages.append({"role": "user", "content": user_msg})
+            messages.append({"role": "assistant", "content": assistant_msg})
+        
+        # 현재 메시지와 파일 내용 추가
+        current_content = message
+        if files:
+            file_contents = []
+            for file in files:
+                content = process_file(file)
+                if content:
+                    file_contents.append(f"파일 내용:\n{content}")
+            if file_contents:
+                current_content = current_content + "\n\n" + "\n\n".join(file_contents)
+        
+        messages.append({"role": "user", "content": current_content})
+        
+        # API 호출 시도
+        try:
             stream = client.chat.completions.create(
                 model="deepseek-ai/DeepSeek-R1",
                 messages=messages,
@@ -123,84 +78,61 @@ def generate_response(
                 top_p=top_p,
                 stream=True
             )
-
-        # Stream response
-        for chunk in stream:
-            if chunk.choices and chunk.choices[0].delta.content:
-                yield chunk.choices[0].delta.content
-
+            
+            for chunk in stream:
+                if chunk.choices and chunk.choices[0].delta.content:
+                    yield chunk.choices[0].delta.content
+                    
+        except Exception as e:
+            if "rate limit" in str(e).lower():
+                yield "죄송합니다. API 호출 한도에 도달했습니다. 잠시 후 다시 시도해주세요."
+            else:
+                yield f"API 호출 중 오류가 발생했습니다: {str(e)}"
+                
     except Exception as e:
-        yield f"Error: {str(e)}"
+        yield f"오류가 발생했습니다: {str(e)}"
+
 def main():
-    st.set_page_config(page_title="DeepSeek Chat", page_icon="💭", layout="wide")
+    st.set_page_config(page_title="DeepSeek 채팅", page_icon="💭", layout="wide")
     
-    # Initialize session state for chat history
     if "messages" not in st.session_state:
         st.session_state.messages = []
 
-    st.title("DeepSeek/Llama Vision Chat with File Upload")
-    st.markdown("Chat with DeepSeek AI model. You can optionally upload files for the model to analyze.")
-    st.markdown("Feel free to upload images too, in this case Llama Vision will be used")
+    st.title("DeepSeek 채팅")
+    st.markdown("DeepSeek AI 모델과 대화하세요. 필요한 경우 파일을 업로드할 수 있습니다.")
 
-    # Sidebar for parameters
     with st.sidebar:
-        st.header("Settings")
+        st.header("설정")
         system_message = st.text_area(
-            "System Message",
-            value="You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside tags, and then provide your solution or response to the problem. 반드시 한글로 답변하라.",
+            "시스템 메시지",
+            value="당신은 깊이 있게 생각하는 AI입니다. 문제를 깊이 고려하고 체계적인 추론 과정을 통해 올바른 해결책을 도출하기 위해 매우 긴 사고 체인을 사용할 수 있습니다. 반드시 한글로 답변하세요.",
             height=100
         )
-        max_tokens = st.slider(
-            "Max Tokens",
-            min_value=1,
-            max_value=8192,
-            value=8192,
-            step=1
-        )
-        temperature = st.slider(
-            "Temperature",
-            min_value=0.1,
-            max_value=4.0,
-            value=0.0,
-            step=0.1
-        )
-        top_p = st.slider(
-            "Top-p (nucleus sampling)",
-            min_value=0.1,
-            max_value=1.0,
-            value=0.95,
-            step=0.05
-        )
+        max_tokens = st.slider("최대 토큰 수", 1, 8192, 8192)
+        temperature = st.slider("온도", 0.1, 4.0, 0.0, 0.1)
+        top_p = st.slider("Top-p", 0.1, 1.0, 0.95, 0.05)
         uploaded_file = st.file_uploader(
-            "Upload File (optional)",
-            type=['txt', 'py', 'md', 'swift', 'java', 'js', 'ts', 'rb', 'go', 
-                 'php', 'c', 'cpp', 'h', 'hpp', 'cs', 'html', 'css', 'kt', 'svelte',
-                 'pdf', 'png', 'jpg', 'jpeg'],  # Added file types
+            "파일 업로드 (선택사항)",
+            type=['txt', 'py', 'md', 'pdf', 'png', 'jpg', 'jpeg'],
             accept_multiple_files=True
         )
 
-    # Display chat messages
     for message in st.session_state.messages:
         with st.chat_message(message["role"]):
             st.write(message["content"])
 
-    # Chat input
-    if prompt := st.chat_input("What would you like to know?"):
-        # Display user message
+    if prompt := st.chat_input("무엇을 알고 싶으신가요?"):
         st.session_state.messages.append({"role": "user", "content": prompt})
         with st.chat_message("user"):
             st.write(prompt)
 
-        # Generate and display assistant response
         with st.chat_message("assistant"):
             response_placeholder = st.empty()
             full_response = ""
             
-            # Get message history for context
             history = [(msg["content"], next_msg["content"]) 
                       for msg, next_msg in zip(st.session_state.messages[::2], st.session_state.messages[1::2])]
             
-            # Stream the response
             for response_chunk in generate_response(
                 prompt,
                 history,
@@ -211,12 +143,10 @@ def main():
                 uploaded_file
             ):
                 full_response += response_chunk
-                print(full_response)
                 response_placeholder.markdown(full_response + "▌")
             
             response_placeholder.markdown(full_response)
         
-        # Add assistant response to chat history
         st.session_state.messages.append({"role": "assistant", "content": full_response})
 
 if __name__ == "__main__":