Update app.py
Browse files
app.py
CHANGED
@@ -8,13 +8,19 @@ import chromadb
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from sentence_transformers import SentenceTransformer
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import google.generativeai as genai
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genai.configure(api_key="AIzaSyAxUd2tS-qj9C7frYuHRsv92tziXHgIvLo")
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CHROMA_PATH = "chroma_db"
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chroma_client = chromadb.PersistentClient(path=CHROMA_PATH)
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collection = chroma_client.get_or_create_collection(name="formula_1")
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embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
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def clean_text(text):
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text = re.sub(r'http\S+', '', text)
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text = re.sub(r'\s+', ' ', text).strip()
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@@ -39,6 +45,8 @@ def scrape_text(url):
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text = clean_text(soup.get_text())
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chunks = split_content_into_chunks(text)
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add_chunks_to_db(chunks)
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return "Scraping and processing complete. You can now ask questions!"
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except requests.exceptions.RequestException as e:
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return f"Error scraping {url}: {e}"
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@@ -47,29 +55,73 @@ def ask_question(query):
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query_embedding = embedding_model.encode(query, convert_to_list=True)
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results = collection.query(query_embeddings=[query_embedding], n_results=2)
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top_chunks = results.get("documents", [[]])[0]
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system_prompt = """
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You are a Formula 1 expert. You answer questions about Formula 1.
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But you only answer based on knowledge I'm providing you. You don't use your internal
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knowledge and you don't make things up.
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If you don't know the answer, just say: I don't know.
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""" + str(top_chunks)
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full_prompt = system_prompt + "\nUser Query: " + query
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model = genai.GenerativeModel('gemini-2.0-flash')
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response = model.generate_content(full_prompt)
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return response.text
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if st.button("Scrape & Process"):
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result = scrape_text(url)
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st.success(result)
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answer = ask_question(query)
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st.write(answer)
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from sentence_transformers import SentenceTransformer
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import google.generativeai as genai
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# Initialize Gemini API
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genai.configure(api_key="AIzaSyAxUd2tS-qj9C7frYuHRsv92tziXHgIvLo")
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# Initialize ChromaDB
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CHROMA_PATH = "chroma_db"
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chroma_client = chromadb.PersistentClient(path=CHROMA_PATH)
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collection = chroma_client.get_or_create_collection(name="formula_1")
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embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
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# Initialize session state to track if scraping is complete
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if 'scraped' not in st.session_state:
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st.session_state.scraped = False
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def clean_text(text):
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text = re.sub(r'http\S+', '', text)
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text = re.sub(r'\s+', ' ', text).strip()
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text = clean_text(soup.get_text())
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chunks = split_content_into_chunks(text)
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add_chunks_to_db(chunks)
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# Set scraped state to True when complete
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st.session_state.scraped = True
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return "Scraping and processing complete. You can now ask questions!"
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except requests.exceptions.RequestException as e:
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return f"Error scraping {url}: {e}"
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query_embedding = embedding_model.encode(query, convert_to_list=True)
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results = collection.query(query_embeddings=[query_embedding], n_results=2)
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top_chunks = results.get("documents", [[]])[0]
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system_prompt = """
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You are a Formula 1 expert. You answer questions about Formula 1.
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But you only answer based on knowledge I'm providing you. You don't use your internal
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knowledge and you don't make things up.
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If you don't know the answer, just say: I don't know.
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""" + str(top_chunks)
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full_prompt = system_prompt + "\nUser Query: " + query
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model = genai.GenerativeModel('gemini-2.0-flash')
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response = model.generate_content(full_prompt)
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return response.text
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# Main UI
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st.title("Formula 1 Web Scraper & Chatbot")
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# Scraping section
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with st.container():
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st.subheader("Step 1: Scrape a Formula 1 Website")
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url = st.text_input("Enter a Formula 1 related URL:")
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if url:
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if st.button("Scrape & Process"):
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with st.spinner("Scraping and processing content..."):
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result = scrape_text(url)
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st.success(result)
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# Q&A section - only appears after scraping is complete
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if st.session_state.scraped:
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with st.container():
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st.subheader("Step 2: Ask Questions About Formula 1")
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st.write("The database contains information scraped from the website. Ask a question about Formula 1:")
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# Chat history
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if 'chat_history' not in st.session_state:
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st.session_state.chat_history = []
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# Display chat history
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for message in st.session_state.chat_history:
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with st.chat_message(message["role"]):
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st.write(message["content"])
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# Input for new question
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user_query = st.chat_input("Ask your Formula 1 question here")
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if user_query:
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# Add user question to chat history
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st.session_state.chat_history.append({"role": "user", "content": user_query})
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# Display user question
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with st.chat_message("user"):
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st.write(user_query)
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# Get and display answer
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with st.chat_message("assistant"):
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with st.spinner("Searching Formula 1 database..."):
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answer = ask_question(user_query)
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st.write(answer)
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# Add answer to chat history
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st.session_state.chat_history.append({"role": "assistant", "content": answer})
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else:
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st.info("Please scrape a Formula 1 website first to populate the database, then you can ask questions!")
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# Add a button to clear the session and start over
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if st.button("Clear Chat History and Data"):
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st.session_state.chat_history = []
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st.session_state.scraped = False
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st.experimental_rerun()
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