| import streamlit as st | |
| from huggingface_hub import InferenceClient | |
| from googlesearch import search | |
| # Function to load the model | |
| def load_model(): | |
| if "client" not in st.session_state: | |
| st.session_state.client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct") | |
| return st.session_state.client | |
| # Load the model once | |
| client = load_model() | |
| # Define prompt templates with the assistant's new persona | |
| def create_prompt(user_message): | |
| return f""" | |
| You are Katheriya, a skilled data scientist who helps users find the best information from around the globe. You are highly knowledgeable and provide insightful, detailed responses. | |
| User: {user_message} | |
| Katheriya: | |
| """ | |
| # Function to process the query using the open-source LLM for general chat | |
| def chat_with_llm(query): | |
| try: | |
| # Create the formatted prompt using the template | |
| formatted_prompt = create_prompt(query) | |
| # Prepare the messages in the required format | |
| messages = [{"role": "user", "content": formatted_prompt}] | |
| # Create an empty container for streaming the response | |
| response_container = st.empty() | |
| response_text = "" | |
| # Stream the response from the model | |
| response_stream = client.chat_completion(messages=messages, stream=True, max_tokens=2048) | |
| for message in response_stream: | |
| # Check if the response has content | |
| if 'choices' in message and message['choices']: | |
| delta_content = message['choices'][0]['delta'].get('content', '') | |
| response_text += delta_content | |
| response_container.write(f"**Katheriya:** {response_text}") # Update response in real-time | |
| return response_text | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| # Function to process the query for search intent | |
| def process_query_with_llm(query): | |
| prompt = f"User asked: '{query}'. What would be the best search query to use?" | |
| # Generate response using text_generation without assuming the structure of the output | |
| response = client.text_generation(prompt) | |
| # Ensure response is in string format and handle errors | |
| if isinstance(response, str): | |
| return response.strip() | |
| elif isinstance(response, list) and 'generated_text' in response[0]: | |
| return response[0]['generated_text'].strip() | |
| else: | |
| return "No query generated." | |
| # Function to perform a Google search using the googlesearch-python package | |
| def search_web(query): | |
| try: | |
| search_results = [] | |
| for result in search(query, num_results=10): | |
| search_results.append(result) | |
| return search_results | |
| except Exception as e: | |
| st.error(f"An error occurred during web search: {e}") | |
| return [] | |
| # Streamlit UI | |
| st.title("Interactive Chatbot - Powered by Katheriya") | |
| # Input field for user query | |
| user_input = st.text_input("You:", "") | |
| if user_input: | |
| st.write(f"**You:** {user_input}") | |
| search_phrases = [ | |
| "search", "find", "get me", "give me", "look up", "show me", | |
| "retrieve", "browse", "where can I find", "search for", "look for", | |
| "can you find", "find me", "what is", "how to", "who is", "where is", | |
| "what are", "tell me about", "do you know", "could you find", | |
| "can you search", "help me find", "explore", "fetch", "locate", "suggest" | |
| ] | |
| # Determine if the query is a search or a general chat | |
| if any(keyword in user_input.lower() for keyword in search_phrases): | |
| # If the user input indicates a search intent | |
| search_query = process_query_with_llm(user_input) | |
| st.write(f"**Processed Query:** {search_query}") | |
| # Search the web using the processed query | |
| links = search_web(search_query) | |
| # Display the search results | |
| if links: | |
| st.write("Here are some links you might find useful:") | |
| for idx, link in enumerate(links): | |
| st.write(f"{idx + 1}. [Link {idx + 1}]({link})") | |
| else: | |
| st.write("Sorry, I couldn't find any relevant links.") | |
| else: | |
| # Handle general conversation with response streaming | |
| response = chat_with_llm(user_input) | |
| # import streamlit as st | |
| # from huggingface_hub import InferenceClient | |
| # from googlesearch import search | |
| # # Initialize the InferenceClient with the model and token | |
| # if 'client' not in st.session_state: | |
| # st.session_state.client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct") | |
| # client = st.session_state.client | |
| # # Function to get chat completion from the model with personalization | |
| # def chat_with_llm(message): | |
| # try: | |
| # # Define the prompt with a name and role | |
| # personalized_message = f""" | |
| # You are Vishal, a data scientist working at Inferenz.ai. Respond to the following query as naturally and informatively as possible. | |
| # User: {message} | |
| # Vishal: | |
| # """ | |
| # response_stream = client.chat_completion(messages=[{"role": "user", "content": personalized_message}], stream=True, max_tokens=500) | |
| # delta_content = "" | |
| # for message in response_stream: | |
| # if 'choices' in message and message['choices']: | |
| # delta_content += message['choices'][0]['delta'].get('content', '') | |
| # return delta_content.strip() | |
| # except Exception as e: | |
| # return f"An error occurred: {e}" | |
| # # Function to process the query for search intent | |
| # def process_query_with_llm(query): | |
| # response = chat_with_llm(f"User asked: '{query}'. What would be the best search query to use?") | |
| # st.write("Query for search:", response) | |
| # return response | |
| # # Function to perform a Google search using the googlesearch-python package | |
| # def search_web(query): | |
| # search_results = [] | |
| # try: | |
| # for result in search(query, num_results=10): | |
| # search_results.append(result) | |
| # except Exception as e: | |
| # st.write(f"An error occurred during the search: {e}") | |
| # return search_results | |
| # # Streamlit UI | |
| # st.title("Interactive Chatbot") | |
| # # Input field for user query | |
| # user_input = st.text_input("You:", "") | |
| # # Check if the input field is not empty | |
| # if user_input: | |
| # st.write(f"**You:** {user_input}") | |
| # search_phrases = [ | |
| # "search", "find", "get me", "give me", "look up", "show me", "retrieve", | |
| # "browse", "where can I find", "search for", "look for", "can you find", | |
| # "find me", "what is", "how to", "who is", "where is", "what are", | |
| # "tell me about", "do you know", "could you find", "can you search", | |
| # "help me find", "explore", "fetch", "locate", "suggest me", "suggest" | |
| # ] | |
| # if any(keyword in user_input.lower() for keyword in search_phrases): | |
| # # If the user input indicates a search intent | |
| # search_query = process_query_with_llm(user_input) | |
| # st.write(f"**Processed Query:** {search_query}") | |
| # # Search the web using the processed query | |
| # links = search_web(search_query) | |
| # # Display the search results | |
| # if links: | |
| # st.write("Here are some links you might find useful:") | |
| # for idx, link in enumerate(links): | |
| # st.write(f"{idx + 1}. [Link {idx + 1}]({link})") | |
| # else: | |
| # st.write("Sorry, I couldn't find any relevant links.") | |
| # else: | |
| # # Handle general conversation | |
| # response = chat_with_llm(user_input) | |
| # st.write(f"**Vishal:** {response}") | |
| # # Ensure input field is cleared after processing | |
| # st.text_input("You:", "", key="user_input") | |