Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""app.py
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1NU6NHjan4eF9IVHR549tKLRVNUQ_dBD7
+"""
+
+import os
+import torch
+import numpy as np
+import requests
+import json
+import gradio as gr
+from dotenv import load_dotenv
+import torch.nn as nn
+
+# ---- Load env variables ----
+# load_dotenv()
+OPENROUTER_KEY = os.getenv("OPENROUTER_KEY")
+if not OPENROUTER_KEY:
+    raise ValueError("OPENROUTER_KEY not set in environment variables.")
+
+# ---- Blackjack Environment ----
+import random
+
+class BlackjackEnv:
+    def __init__(self):
+        self.dealer = []
+        self.player = []
+        self.usable_ace_player = False
+
+    def draw_card(self):
+        return random.randint(1, 10)
+
+    def sum_hand(self, hand):
+        total = sum(hand)
+        ace = 1 in hand
+        if ace and total + 10 <= 21:
+            return total + 10, True
+        return total, False
+
+    def reset(self):
+        self.player = [self.draw_card(), self.draw_card()]
+        self.dealer = [self.draw_card()]
+        total, usable_ace = self.sum_hand(self.player)
+        self.usable_ace_player = usable_ace
+        return (self.dealer[0], total, int(usable_ace))
+
+    def step(self, action):
+        if action == 1:
+            self.player.append(self.draw_card())
+            total, usable_ace = self.sum_hand(self.player)
+            if total > 21:
+                return (self.dealer[0], total, int(usable_ace)), -1, True
+            return (self.dealer[0], total, int(usable_ace)), 0, False
+        else:
+            dealer_hand = self.dealer + [self.draw_card()]
+            dealer_total, _ = self.sum_hand(dealer_hand)
+            player_total, _ = self.sum_hand(self.player)
+            if dealer_total < player_total:
+                return (self.dealer[0], player_total, int(self.usable_ace_player)), 1, True
+            elif dealer_total > player_total:
+                return (self.dealer[0], player_total, int(self.usable_ace_player)), -1, True
+            else:
+                return (self.dealer[0], player_total, int(self.usable_ace_player)), 0, True
+
+# ---- QNetwork ----
+class QNetwork(nn.Module):
+    def __init__(self, state_size=3, hidden_size=128, action_size=2):
+        super(QNetwork, self).__init__()
+        self.model = nn.Sequential(
+            nn.Linear(state_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, action_size)
+        )
+
+    def forward(self, x):
+        return self.model(x)
+
+# ---- Load model ----
+model = QNetwork()
+model_path = "/content/sample_data/qnetwork_blackjack_weights.pth"
+model.load_state_dict(torch.load(model_path))
+model.eval()
+
+env = BlackjackEnv()
+
+# ---- LLM Explanation ----
+def explain_action(state, action):
+    prompt = f"""
+You are a blackjack strategy explainer. The player has a total of {state[1]}.
+The dealer is showing {state[0]}. Usable ace: {bool(state[2])}.
+The DQN model chose to {'Hit' if action == 1 else 'Stick'}.
+Explain why this action makes sense in 2-3 sentences.
+"""
+
+    headers = {
+        "Authorization": f"Bearer {OPENROUTER_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    data = {
+        "model": "mistralai/mistral-7b-instruct",
+        "messages": [
+            {"role": "system", "content": "You explain blackjack strategies clearly."},
+            {"role": "user", "content": prompt}
+        ]
+    }
+
+    try:
+        response = requests.post("https://openrouter.ai/api/v1/chat/completions",
+                                 headers=headers, data=json.dumps(data))
+        if response.status_code == 200:
+            return response.json()['choices'][0]['message']['content']
+        return f"LLM error: {response.status_code} - {response.text}"
+    except Exception as e:
+        return f"LLM call failed: {str(e)}"
+
+# ---- Gradio App ----
+def play_hand():
+    state = env.reset()
+    state_tensor = torch.tensor(state, dtype=torch.float32).unsqueeze(0)
+    with torch.no_grad():
+        q_values = model(state_tensor)
+    action = torch.argmax(q_values).item()
+
+    explanation = explain_action(state, action)
+    action_name = "Hit" if action == 1 else "Stick"
+    dealer_card, player_sum, usable_ace = state
+
+    return [
+        str(player_sum),
+        str(dealer_card),
+        str(bool(usable_ace)),
+        action_name,
+        str(q_values.numpy().tolist()),
+        explanation
+    ]
+
+demo = gr.Interface(
+    fn=play_hand,
+    inputs=[],
+    outputs=[
+        gr.Textbox(label="Player Sum"),
+        gr.Textbox(label="Dealer Card"),
+        gr.Textbox(label="Usable Ace"),
+        gr.Textbox(label="DQN Action"),
+        gr.Textbox(label="Q-values"),
+        gr.Textbox(label="LLM Explanation")
+    ],
+    title="🧠 Blackjack Tutor: DQN + LLM",
+    description="Play a hand of blackjack. See how a Deep Q Network plays, and get a natural language explanation from Mistral-7B via OpenRouter."
+)
+
+if __name__ == "__main__":
+    demo.launch()
+
+import os
+print(os.listdir())