Create app.py

app.py

@@ -0,0 +1,71 @@
+from llama_cpp import Llama
+from typing import Optional, Dict, Union
+from huggingface_hub import hf_hub_download
+import gradio as gr
+import time
+
+
+
+model_path = hf_hub_download(
+    repo_id="omeryentur/phi-3-sql",
+    filename="phi-3-sql.Q4_K_M.gguf",
+    use_auth_token=True
+)
+
+llm = Llama(
+    model_path=model_path,
+    n_ctx=512,
+    n_threads=1,
+)
+
+def generate_sql_query(text_input_schema:str,text_input_question: str):
+    global pattern_counter
+    
+    if not text:
+        return {"error": "not found text"}, None, None
+    try:
+        prompt = f"""<|system|>
+            {table_info}
+
+            <|user|>
+            {question}
+
+            <|sql|>"""
+        
+        completion = llm(
+            prompt,
+            max_tokens=512,
+            temperature=0,
+            stop=["<end_of_turn>"]
+        )
+        
+        generated_pattern = completion['choices'][0]['text'].strip()
+        result = parse_log_with_grok(text, generated_pattern)
+        time.sleep(0.5)
+            
+        return result
+        
+    except Exception as e:
+        return {"error": e}
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Sql Query")
+    
+    with gr.Row():
+        with gr.Column():
+            text_input_schema = gr.Textbox(label="Schema")
+            text_input_question = gr.Textbox(label="question")
+            generate_btn = gr.Button("Create Sql Query")
+        
+    with gr.Row():
+        with gr.Column():
+            output = gr.JSON(label="Sql Query:")
+    
+    generate_btn.click(
+        fn=generate_sql_query,
+        inputs=[text_input_schema,text_input_question],
+        outputs=[pattern_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()