# -*- coding: utf-8 -*- import os import json import requests import tempfile import gradio as gr # ---------------------------------------------------------------------- # Configuration # ---------------------------------------------------------------------- GROQ_API_KEY = os.environ.get("GROQ_API_KEY") GROQ_ENDPOINT = "https://api.groq.com/openai/v1/chat/completions" MODEL_NAME = os.environ.get("MODEL_NAME", "llama-3.1-8b-instant") # ---------------------------------------------------------------------- # CSS (will be passed to demo.launch) # ---------------------------------------------------------------------- CSS = """ #business-input textarea { border: 1px solid #CCCCCC; } #business-input textarea:focus { border-color: #1E90FF; } #generate-btn { background-color: #1E90FF; color: white; } #generate-btn:hover { background-color: #1C86EE; } #problems-card, #solutions-card, #explanation-card, #prompt-card { padding: 15px; border-radius: 8px; margin-top: 10px; } #problems-card { background-color: #F0F8FF; } #solutions-card { background-color: #E6F7FF; } #explanation-card { background-color: #FFFFFF; } #prompt-card { background-color: #F5F5F5; } #download-btn { margin-top: 10px; } """ # ---------------------------------------------------------------------- # Helper functions # ---------------------------------------------------------------------- def call_groq(business_name: str) -> dict: """ Calls the Groq API and returns a dict with keys: problems, solutions, explanation, eos_prompt. """ if not GROQ_API_KEY: raise RuntimeError("GROQ_API_KEY not set in environment") user_prompt = f"""Eres un analista de negocios experto. Dado el nombre del negocio "{business_name}", haz una lista de los 5 problemas clasicos principales, propone una solucion para cada uno, ofrece una explicacion detallada combinada y, por ultimo, genera un prompt de EOS listo para usar. Toda la respuesta debe estar redactada en español, pero estructurada estrictamente en el siguiente formato JSON (manteniendo las claves en ingles para la lectura del sistema): {{ "problems": ["...", "...", "..."], "solutions": ["...", "...", "..."], "explanation": "...", "eos_prompt": "Titulo: ...\\nContexto: ...\\nInstrucciones: ..." }} Devuelve unica y exclusivamente el objeto JSON, sin texto introductorio ni conclusiones.""" payload = { "model": MODEL_NAME, "messages": [{"role": "user", "content": user_prompt}], "temperature": 0.7, "max_tokens": 1024, } headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } response = requests.post( GROQ_ENDPOINT, headers=headers, json=payload, timeout=30, ) response.raise_for_status() data = response.json() content = data["choices"][0]["message"]["content"].strip() # Remove possible markdown fences backticks = "\x60\x60\x60" if content.startswith(backticks): parts = content.split(backticks) if len(parts) >= 3: content = parts[1] if content.lstrip().startswith("json"): content = content.lstrip()[4:] content = content.strip() return json.loads(content) def format_markdown_list(items): return "\n".join([f"{i+1}. {item}" for i, item in enumerate(items)]) def generate(business_name): if not business_name or not business_name.strip(): err_md = "### Error: El nombre del negocio no puede estar vacio." return err_md, err_md, err_md, err_md, None try: result = call_groq(business_name.strip()) problems = result.get("problems", []) solutions = result.get("solutions", []) explanation = result.get("explanation", "") eos_prompt = result.get("eos_prompt", "") if not (problems and solutions and explanation and eos_prompt): raise ValueError("Respuesta incompleta del modelo.") problems_md = f"### Problemas\n{format_markdown_list(problems)}" solutions_md = f"### Soluciones\n{format_markdown_list(solutions)}" explanation_md = f"### Explicacion\n{explanation}" eos_prompt_code = eos_prompt export_dict = { "business_name": business_name.strip(), "problems": problems, "solutions": solutions, "explanation": explanation, "eos_prompt": eos_prompt, } tmp_file = tempfile.NamedTemporaryFile( delete=False, suffix=".json", mode="w", encoding="utf-8" ) json.dump(export_dict, tmp_file, ensure_ascii=False, indent=2) tmp_file.close() return problems_md, solutions_md, explanation_md, eos_prompt_code, tmp_file.name except requests.exceptions.Timeout: err_md = "### Error: Tiempo de espera agotado. Intenta de nuevo." return err_md, err_md, err_md, err_md, None except requests.exceptions.RequestException as e: err_md = f"### Error al conectar con Groq: {e}" return err_md, err_md, err_md, err_md, None except (json.JSONDecodeError, ValueError) as e: err_md = f"### Error al procesar la respuesta: {e}" return err_md, err_md, err_md, err_md, None except Exception as e: err_md = f"### Error inesperado: {e}" return err_md, err_md, err_md, err_md, None # ---------------------------------------------------------------------- # Gradio Interface # ---------------------------------------------------------------------- with gr.Blocks() as demo: gr.Markdown( "

" "Analizador de Problemas de Negocio

" ) with gr.Row(): business_input = gr.Textbox( label="Nombre del negocio", placeholder="Introduce el nombre del negocio", lines=1, max_lines=1, elem_id="business-input", ) generate_btn = gr.Button( "Generar", variant="primary", elem_id="generate-btn", ) with gr.Column(): problems_out = gr.Markdown(elem_id="problems-card") solutions_out = gr.Markdown(elem_id="solutions-card") explanation_out = gr.Markdown(elem_id="explanation-card") eos_prompt_out = gr.Code( label="Prompt EOS", language="markdown", elem_id="prompt-card", ) download_btn = gr.DownloadButton( label="Descargar JSON", value=None, elem_id="download-btn", ) gr.Markdown( "Mas informacion: " "Documentacion Groq", elem_id="footer", ) generate_btn.click( fn=generate, inputs=[business_input], outputs=[problems_out, solutions_out, explanation_out, eos_prompt_out, download_btn], ) # Launch with theme and css inside launch as required by the platform demo.launch(theme=gr.themes.Soft(), css=CSS)