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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import os | |
| import requests | |
| from datetime import datetime | |
| SPORTS_IO_API_KEY = os.getenv("SPORTS_API_KEY") | |
| NEWS_URL = f"https://api.sportsdata.io/v3/nfl/scores/json/News?key={SPORTS_IO_API_KEY}" | |
| PLAYERS_URL = f"https://api.sportsdata.io/v3/nfl/scores/json/Players?key={SPORTS_IO_API_KEY}" | |
| PLAYER_STATS_URL = "https://api.sportsdata.io/v3/nfl/stats/json/PlayerSeasonStatsByPlayerID/{season}/{playerid}?key={api_key}" | |
| CURRENT_SEASON = 2025 | |
| #headers = {"Ocp-Apim-Subscription-Key": SPORTS_IO_API_KEY} | |
| # Fancy styling | |
| fancy_css = """ | |
| #main-container { | |
| background-color: #f0f0f0; | |
| font-family: 'Arial', sans-serif; | |
| } | |
| .gradio-container { | |
| max-width: 700px; | |
| margin: 0 auto; | |
| padding: 20px; | |
| background: white; | |
| box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); | |
| border-radius: 10px; | |
| } | |
| .gr-button { | |
| background-color: #4CAF50; | |
| color: white; | |
| border: none; | |
| border-radius: 5px; | |
| padding: 10px 20px; | |
| cursor: pointer; | |
| transition: background-color 0.3s ease; | |
| } | |
| .gr-button:hover { | |
| background-color: #45a049; | |
| } | |
| .gr-slider input { | |
| color: #4CAF50; | |
| } | |
| .gr-chat { | |
| font-size: 16px; | |
| } | |
| #title { | |
| text-align: center; | |
| font-size: 2em; | |
| margin-bottom: 20px; | |
| color: #333; | |
| } | |
| """ | |
| def fetch_news(query: str): | |
| try: | |
| resp = requests.get(NEWS_URL, timeout=10) | |
| resp.raise_for_status() | |
| articles = resp.json() | |
| except Exception as e: | |
| return f"⚠️ News API error: {e}" | |
| results = [] | |
| for article in articles: | |
| print(article.get("Title", "")) | |
| if query.lower() in (article.get("Title", "") + article.get("Content", "")).lower(): | |
| date = datetime.fromisoformat(article["Updated"]) | |
| results.append({ | |
| "title": article["Title"], | |
| "content": article["Content"], | |
| "source": article["Source"], | |
| "updated": date.strftime("%B %d, %Y %I:%M %p") | |
| }) | |
| if not results: | |
| return f"No news found for '{query}'." | |
| results = sorted(results, key=lambda x: x["updated"], reverse=True)[:3] | |
| formatted = "### 📰 Latest News\n" | |
| for r in results: | |
| formatted += f"**{r['title']}** \n{r['content'][:250]}... \n" | |
| formatted += f"_Source: {r['source']} • {r['updated']}_\n\n" | |
| return formatted | |
| def fetch_player_stats(player_name: str): | |
| try: | |
| resp = requests.get(PLAYERS_URL, timeout=15) | |
| resp.raise_for_status() | |
| players = resp.json() | |
| except Exception as e: | |
| return f"⚠️ Player lookup error: {e}" | |
| # Find player by name | |
| player = next((p for p in players if player_name.lower() in p["Name"].lower()), None) | |
| if not player: | |
| return None # Not a player | |
| player_id = player["PlayerID"] | |
| team = player.get("Team", "Unknown") | |
| position = player.get("Position", "N/A") | |
| # Fetch season stats | |
| try: | |
| stats_url = PLAYER_STATS_URL.format(season=CURRENT_SEASON, playerid=player_id, api_key = SPORTS_IO_API_KEY) | |
| resp = requests.get(stats_url, timeout=10) | |
| resp.raise_for_status() | |
| stats = resp.json() | |
| except Exception as e: | |
| return f"⚠️ Stats fetch error: {e}" | |
| if not stats: | |
| return f"📊 No stats available yet for {player_name} ({CURRENT_SEASON})." | |
| formatted = f"### 📊 {player_name} ({team}, {position}) — {CURRENT_SEASON} Season Stats\n" | |
| if position in ["QB", "Quarterback"]: | |
| formatted += f"- Passing Yards: {stats[0].get('PassingYards', 0)}\n" | |
| formatted += f"- Passing TDs: {stats[0].get('PassingTouchdowns', 0)}\n" | |
| formatted += f"- Interceptions: {stats[0].get('PassingInterceptions', 0)}\n" | |
| elif position in ["RB", "Running Back"]: | |
| formatted += f"- Rushing Yards: {stats[0].get('RushingYards', 0)}\n" | |
| formatted += f"- Rushing TDs: {stats[0].get('RushingTouchdowns', 0)}\n" | |
| elif position in ["WR", "TE"]: | |
| formatted += f"- Receiving Yards: {stats[0].get('ReceivingYards', 0)}\n" | |
| formatted += f"- Receiving TDs: {stats[0].get('ReceivingTouchdowns', 0)}\n" | |
| formatted += f"- Receptions: {stats[0].get('Receptions', 0)}\n" | |
| else: | |
| formatted += f"- Games Played: {stats[0].get('Played', 0)}\n" | |
| formatted += f"- Fantasy Points: {stats[0].get('FantasyPoints', 0)}\n" | |
| return formatted | |
| def nfl_chatbot(query: str, history: list): | |
| news = fetch_news(query) | |
| stats = fetch_player_stats(query) | |
| if stats is None: | |
| reply = f"{news}\n\n(ℹ️ No player stats — likely a coach.)" | |
| else: | |
| reply = f"{news}\n\n{stats}" | |
| history.append((query, reply)) | |
| return history, "" | |
| with gr.Blocks(css=fancy_css) as demo: | |
| gr.Markdown("# 🏈 NFL News + Stats Chatbot") | |
| gr.Markdown("Ask about any NFL player or coach for the **latest news and stats**.") | |
| chatbot = gr.Chatbot() | |
| query = gr.Textbox(label="Enter player or coach name") | |
| stats_btn = gr.Button("Get Stats") | |
| news_btn = gr.Button("Get News") | |
| stats_btn.click(nfl_chatbot, [query, chatbot], [chatbot, query]) | |
| query.submit(nfl_chatbot, [query, chatbot], [chatbot, query]) | |
| if __name__ == "__main__": | |
| demo.launch() | |
| ''' | |
| pipe = None | |
| stop_inference = False | |
| def respond( | |
| message, | |
| history: list[dict[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| hf_token: gr.OAuthToken, | |
| use_local_model: bool, | |
| ): | |
| global pipe | |
| # Build messages from history | |
| messages = [{"role": "system", "content": system_message}] | |
| messages.extend(history) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| if use_local_model: | |
| print("[MODE] local") | |
| from transformers import pipeline | |
| import torch | |
| if pipe is None: | |
| pipe = pipeline("text-generation", model="microsoft/Phi-3-mini-4k-instruct") | |
| # Build prompt as plain text | |
| prompt = "\n".join([f"{m['role']}: {m['content']}" for m in messages]) | |
| outputs = pipe( | |
| prompt, | |
| max_new_tokens=max_tokens, | |
| do_sample=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ) | |
| response = outputs[0]["generated_text"][len(prompt):] | |
| yield response.strip() | |
| else: | |
| print("[MODE] api") | |
| if hf_token is None or not getattr(hf_token, "token", None): | |
| yield "⚠️ Please log in with your Hugging Face account first." | |
| return | |
| client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b") | |
| for chunk in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| choices = chunk.choices | |
| token = "" | |
| if len(choices) and choices[0].delta.content: | |
| token = choices[0].delta.content | |
| response += token | |
| yield response | |
| chatbot = gr.ChatInterface( | |
| fn=respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), | |
| gr.Checkbox(label="Use Local Model", value=False), | |
| ], | |
| type="messages", | |
| ) | |
| with gr.Blocks(css=fancy_css) as demo: | |
| with gr.Row(): | |
| gr.Markdown("<h1 style='text-align: center;'>🌟 Fancy AI Chatbot 🌟</h1>") | |
| gr.LoginButton() | |
| chatbot.render() | |
| if __name__ == "__main__": | |
| demo.launch() | |
| ''' |