import streamlit as st import requests import json from duckduckgo_search import DDGS import re import clipboard import io import os import pandas as pd from PyPDF2 import PdfFileReader from PIL import Image import pytesseract from bs4 import BeautifulSoup import tiktoken from langchain import LLMChain, PromptTemplate # OpenRouter APIキーの設定 OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY").strip() # Streamlitアプリケーションの設定 st.set_page_config(page_title="Orb AI", page_icon="🤖", layout="wide") # セッション状態の初期化 if 'messages' not in st.session_state: st.session_state.messages = [] if 'uploaded_file_content' not in st.session_state: st.session_state.uploaded_file_content = None def read_pdf(file): pdf = PdfFileReader(file) content = "" for page_num in range(pdf.getNumPages()): page = pdf.getPage(page_num) content += page.extract_text() return content def read_csv(file): df = pd.read_csv(file) return df.to_string() def read_image(file): image = Image.open(file) text = pytesseract.image_to_string(image) return text def fetch_webpage_text(url): response = requests.get(url) if response.status_code != 200: return "Web page could not be retrieved" soup = BeautifulSoup(response.text, 'html.parser') paragraphs = soup.find_all('p') text_content = ' '.join([paragraph.get_text() for paragraph in paragraphs]) return text_content def truncate_prompt(prompt, max_tokens): tokenizer = tiktoken.get_encoding("cl100k_base") tokens = tokenizer.encode(prompt) if len(tokens) > max_tokens: truncated_tokens = tokens[:max_tokens] truncated_prompt = tokenizer.decode(truncated_tokens) return truncated_prompt return prompt def call_openrouter_api(prompt, model): max_context_tokens = 4096 completion_tokens = 500 max_prompt_tokens = max_context_tokens - completion_tokens truncated_prompt = truncate_prompt(prompt, max_prompt_tokens) response = requests.post( url="https://openrouter.ai/api/v1/chat/completions", headers={ "Authorization": f"Bearer {OPENROUTER_API_KEY}", "HTTP-Referer": "https://supertakerin2-comcomgptfree.hf.space/", "X-Title": "Orb AI", }, data=json.dumps({ "model": model, "messages": [{"role": "user", "content": truncated_prompt}] }) ) if response.status_code == 200: try: response_json = response.json() if "choices" in response_json: return response_json["choices"][0]["message"]["content"] else: st.error("API response does not contain 'choices'") return "API response does not contain 'choices'" except json.JSONDecodeError: st.error("API response JSON decoding failed.") return "API response JSON decoding failed." else: st.error(f"API request failed status code: {response.status_code}, message: {response.text}") return "API request failed" # サイドバーにAIモデルの選択、モード選択、ファイルアップロードを配置 with st.sidebar: ai_model = st.selectbox( "Select AI model", ["openchat/openchat-7b:free", "microsoft/phi-3-mini-128k-instruct:free", "meta-llama/llama-3.1-8b-instruct:free", "google/gemma-7b-it:free", "undi95/toppy-m-7b:free", "mistralai/mistral-7b-instruct:free"], key="model_select" ) mode = st.selectbox( "Select mode", ["focus", "writing", "video", "summary"], key="mode_select" ) # ファイルアップロード機能をサイドバーに移動 uploaded_file = st.file_uploader("Upload file", type=["txt", "pdf", "csv", "py", "html", "css", "js", "cs", "php", "java", "jpg"]) if uploaded_file is not None: file_extension = uploaded_file.name.split('.')[-1].lower() if file_extension in ['txt', 'py', 'html', 'css', 'js', 'cs', 'php', 'java']: stringio = io.StringIO(uploaded_file.getvalue().decode("utf-8")) st.session_state.uploaded_file_content = stringio.read() elif file_extension == 'pdf': st.session_state.uploaded_file_content = read_pdf(uploaded_file) elif file_extension == 'csv': st.session_state.uploaded_file_content = read_csv(uploaded_file) elif file_extension in ['jpg']: st.session_state.uploaded_file_content = read_image(uploaded_file) st.success(f"file '{uploaded_file.name}' was uploaded") # 会話をクリアするボタン if st.button("🔥 Delete conversation"): st.session_state.messages = [] st.session_state.uploaded_file_content = None # メインコンテンツ st.title("Orb AI") # チャット履歴の表示 for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) # ユーザー入力 if prompt := st.chat_input("Please enter your message."): st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.markdown(prompt) # アップロードされたファイルの内容を考慮してプロンプトを作成 if st.session_state.uploaded_file_content: prompt = f"以下のファイル内容を参考にして回答してください:\n\n{st.session_state.uploaded_file_content}\n\nユーザーの質問: {prompt}" full_response = "" if mode == "focus": # DuckDuckGo検索の実行 with DDGS() as ddgs: search_results = list(ddgs.text(prompt, max_results=4)) search_results_text = "\n".join([result["body"] for result in search_results]) urls = "\n".join([result["href"] for result in search_results[:4]]) prompt = f"質問: {prompt}\n\n検索結果: {search_results_text}\n\n上記の情報に基づいて、詳細な回答を提供してください：" full_response = call_openrouter_api(prompt, ai_model) # 回答の下に検索結果のURLを表示 with st.chat_message("assistant"): st.markdown(full_response) st.markdown("### Sources:") st.markdown(urls) elif mode == "writing": prompt = f"次のトピックについて包括的な記事を書いてください: {prompt}\n\n導入、主要なポイント、および結論を含めてください。" full_response = call_openrouter_api(prompt, ai_model) with st.chat_message("assistant"): st.markdown(full_response) elif mode == "summary": prompt = f"次のテキストを簡潔に要約してください:\n\n{prompt}\n\n主要なポイントを捉えた簡潔な要約を提供してください。" full_response = call_openrouter_api(prompt, ai_model) with st.chat_message("assistant"): st.markdown(full_response) elif mode == "video": # DuckDuckGo検索を使用してYouTubeの動画を検索 with DDGS() as ddgs: search_results = list(ddgs.text(f"{prompt} site:youtube.com", max_results=5)) if search_results: for result in search_results[:4]: video_url = result['href'] video_id = None youtube_patterns = [ r"(?<=v=)[^/]+", r"(?<=be/)[^/]+", r"(?<=embed/)[^/]+" ] for pattern in youtube_patterns: match = re.search(pattern, video_url) if match: video_id = match.group() break if video_id: st.markdown(f'', unsafe_allow_html=True) st.markdown(f"Related Videos: {result['title']}") else: st.warning(f"Video ID not found: {video_url}") else: full_response = "No related videos were found." with st.chat_message("assistant"): st.markdown(full_response) # コピー機能の追加 if st.button("📋 Copy the generated text"): clipboard.copy(full_response) st.success("Text has been copied to the clipboard!")