Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModel, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "TuringsSolutions/TechLegalV1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+# Function to make predictions
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    # Assuming we need to extract some specific information from outputs
+    # Modify this part based on your model's output format
+    return outputs.last_hidden_state.mean(dim=1).squeeze().tolist()
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="json",
+    title="Tech Legal Model",
+    description="A model for analyzing tech legal documents."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()