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app.py

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+
+import gradio as gr
+import torch
+import torch.nn as nn
+
+# Define your custom model class
+class BigramLanguageModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # Example layers (adjust as needed for your model)
+        self.token_embedding_table = nn.Embedding(61, 64)
+        self.position_embedding_table = nn.Embedding(32, 64)
+        self.blocks = nn.Sequential(*[nn.Linear(64, 64) for _ in range(4)])
+        self.ln_f = nn.LayerNorm(64)
+        self.lm_head = nn.Linear(64, 61)
+
+    def forward(self, idx):
+        # Implement the forward pass
+        pass
+
+    def generate(self, idx, max_new_tokens=250):
+        # Implement the generate method
+        pass
+
+# Load your model
+def load_model():
+    model = BigramLanguageModel()
+    model_url = "https://huggingface.co/yoonusajwardapiit/triptuner/resolve/main/pytorch_model.bin"
+    model_weights = torch.hub.load_state_dict_from_url(model_url, map_location=torch.device('cpu'), weights_only=True)
+    model.load_state_dict(model_weights)
+    model.eval()
+    return model
+
+model = load_model()
+
+# Define encode and decode functions
+chars = sorted(list(set("your_training_text_here")))  # Replace with the character set used in training
+stoi = {ch: i for i, ch in enumerate(chars)}
+itos = {i: ch for i, ch in enumerate(chars)}
+encode = lambda s: [stoi[c] for c in s]
+decode = lambda l: ''.join([itos[i] for i in l])
+
+# Function to generate text using the model
+def generate_text(prompt):
+    context = torch.tensor([encode(prompt)], dtype=torch.long)
+    with torch.no_grad():
+        generated = model.generate(context, max_new_tokens=250)  # Adjust as needed
+    return decode(generated[0].tolist())
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a location or prompt..."),
+    outputs="text",
+    title="Triptuner Model",
+    description="Generate itineraries for locations in Sri Lanka's Central Province."
+)
+
+# Launch the interface
+interface.launch()

requirements.txt

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+torch
+gradio