Update app.py

app.py

@@ -1,10 +1,47 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 import os
+import requests
 
-# Use a strong model that works well with streaming
 client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1", token=os.getenv("HF_TOKEN"))
 
+# Anime API fetch function
+def fetch_anime_info(title):
+    query = '''
+    query ($search: String) {
+      Media(search: $search, type: ANIME) {
+        title {
+          romaji
+          english
+        }
+        description(asHtml: false)
+        episodes
+        status
+        averageScore
+        genres
+        siteUrl
+      }
+    }
+    '''
+    variables = {"search": title}
+    response = requests.post(
+        "https://graphql.anilist.co",
+        json={"query": query, "variables": variables}
+    )
+    if response.status_code == 200:
+        data = response.json()["data"]["Media"]
+        return f"""
+**Title**: {data['title']['english'] or data['title']['romaji']}
+**Episodes**: {data['episodes']}
+**Status**: {data['status']}
+**Score**: {data['averageScore']}
+**Genres**: {', '.join(data['genres'])}
+**Description**: {data['description']}
+**Link**: {data['siteUrl']}
+""".strip()
+    else:
+        return "Sorry, I couldn’t find info on that anime!"
+
 def format_prompt(system_message, history, user_input):
     prompt = f"[System Message]: {system_message.strip()}\n\n"
     for user, bot in history:
@@ -12,17 +49,24 @@ def format_prompt(system_message, history, user_input):
     prompt += f"[User]: {user_input}\n[Assistant]:"
     return prompt
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    prompt = format_prompt(system_message, history, message)
-    response = ""
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
+    # If the user asks for anime info directly
+    if message.lower().startswith("anime "):
+        title = message[6:].strip()
+        response = fetch_anime_info(title)
+        yield response
+        return
 
+    # Normal LLM prompt
+    # Add Luna's personality and capability description to system message
+    luna_system_message = (
+        "Your name is Luna, a helpful anime chatbot created by Reiker. "
+        "You're capable of answering questions and explaining anime-related content. "
+        "If someone asks about an anime, try to respond naturally with the info you know. "
+        + system_message.strip()
+    )
+    prompt = format_prompt(luna_system_message, history, message)
+    response = ""
     for token in client.text_generation(
         prompt,
         max_new_tokens=max_tokens,
@@ -36,18 +80,12 @@ def respond(
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are Reiker, a helpful anime-style chatbot.", label="System message"),
+        gr.Textbox(value="You are Luna, a helpful anime-style chatbot created by Reiker.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, 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.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
-    title="Reiker AI"
+    title="Luna - Reiker's Anime AI"
 )
 
 if __name__ == "__main__":