import os from dotenv import load_dotenv import gradio as gr from haystack import Pipeline from haystack.utils import Secret from haystack.components.fetchers import LinkContentFetcher from haystack.components.converters import HTMLToDocument from haystack.components.builders import PromptBuilder from haystack.components.generators import OpenAIGenerator load_dotenv() # api_key = os.getenv('API_KEY') MODEL = "microsoft/Phi-3-mini-4k-instruct" # Set up components fetcher = LinkContentFetcher() converter = HTMLToDocument() prompt_template = """ According to the contents of this website: {% for document in documents %} {{document.content}} {% endfor %} Answer the given question: {{query}} Answer: """ prompt_builder = PromptBuilder(template=prompt_template) llm = OpenAIGenerator( api_key=Secret.from_env_var("MONSTER_API_KEY"), api_base_url="https://llm.monsterapi.ai/v1/", model=MODEL, generation_kwargs={"max_tokens": 256} ) pipeline = Pipeline() pipeline.add_component("fetcher", fetcher) pipeline.add_component("converter", converter) pipeline.add_component("prompt", prompt_builder) pipeline.add_component("llm", llm) pipeline.connect("fetcher.streams", "converter.sources") pipeline.connect("converter.documents", "prompt.documents") pipeline.connect("prompt.prompt", "llm.prompt") # Function to handle the chat and query def answer_query(url, query): result = pipeline.run({"fetcher": {"urls": [url]}, "prompt": {"query": query}}) return result["llm"]["replies"][0] # Gradio interface def chat_interface(url, query): return answer_query(url, query) with gr.Blocks() as demo: gr.Markdown("# Indian 2024 Budget Chatbot") url_input = gr.Textbox(label="Enter URL with Budget Details") query_input = gr.Textbox(label="Enter Your Question") submit_button = gr.Button("Get Answer") output_text = gr.Textbox(label="Answer", interactive=False) submit_button.click(fn=chat_interface, inputs=[url_input, query_input], outputs=output_text) # Run the app locally if __name__ == "__main__": demo.launch()