Create app.py

app.py

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+from dotenv import load_dotenv
+
+load_dotenv()
+
+api = os.getenv("groq_api_key")
+
+
+from sentence_transformers import SentenceTransformer
+import gradio as gr
+from sklearn.metrics.pairwise import cosine_similarity
+from groq import Groq
+
+def create_metadata_embeddings():
+  student="""
+  Table: student
+  Columns:
+  - student_id: an integer representing the unique ID of a student.
+  - first_name: a string containing the first name of the student.
+  - last_name: a string containing the last name of the student.
+  - date_of_birth: a date representing the student's birthdate.
+  - email: a string for the student's email address.
+  - phone_number: a string for the student's contact number.
+  - major: a string representing the student's major field of study.
+  - year_of_enrollment: an integer for the year the student enrolled.
+  """
+
+  employee="""
+  Table: employee
+  Columns:
+  - employee_id: an integer representing the unique ID of an employee.
+  - first_name: a string containing the first name of the employee.
+  - last_name: a string containing the last name of the employee.
+  - email: a string for the employee's email address.
+  - department: a string for the department the employee works in.
+  - position: a string representing the employee's job title.
+  - salary: a float representing the employee's salary.
+  - date_of_joining: a date for when the employee joined the college.
+  """
+
+  course="""
+  Table: course_info
+  Columns:
+  - course_id: an integer representing the unique ID of the course.
+  - course_name: a string containing the course's name.
+  - course_code: a string for the course's unique code.
+  - instructor_id: an integer for the ID of the instructor teaching the course.
+  - department: a string for the department offering the course.
+  - credits: an integer representing the course credits.
+  - semester: a string for the semester when the course is offered.
+  """
+
+  metadata_list = [student, employee, course]
+
+  model = SentenceTransformer('all-MiniLM-L6-v2')
+
+  embeddings = model.encode(metadata_list)
+
+  return embeddings,model,student,employee,course
+
+def find_best_fit(embeddings,model,user_query,student,employee,course):
+  query_embedding = model.encode([user_query])
+  similarities = cosine_similarity(query_embedding, embeddings)
+  best_match_table = similarities.argmax()
+  if(best_match_table==0):
+    table_metadata=student
+  elif(best_match_table==1):
+    table_metadata=employee
+  else:
+    table_metadata=course
+
+  return table_metadata
+
+
+
+def create_prompt(user_query,table_metadata):
+  system_prompt="""
+  You are a SQL query generator specialized in generating SELECT queries for a single table at a time. Your task is to accurately convert natural language queries into SQL SELECT statements based on the user's intent and the provided table metadata.
+  Rules:
+  Focus on SELECT Queries: Only generate SELECT queries. Do not generate INSERT, UPDATE, DELETE, or multi-table JOINs.
+  Single Table Only: Assume all queries are related to a single table provided in the metadata. Ignore any references to other tables.
+  Metadata-Based Validation: Always ensure the generated query matches the table name, columns, and data types provided in the metadata.
+  User Intent: Accurately capture the user's requirements, such as filters, sorting, or aggregations, as expressed in natural language.
+  SQL Syntax: Use standard SQL syntax that is compatible with most relational database systems.
+  Input Format:
+  User Query: The user's natural language request.
+  Table Metadata: The structure of the relevant table, including the table name, column names, and data types.
+  Output Format:
+  SQL Query: A valid SELECT query formatted for readability.
+  Do not output anything else except the SQL query.Not even a single word extra.Ouput the whole query in a single line only.
+  You are ready to generate SQL queries based on the user input and table metadata.
+  """
+
+
+  user_prompt=f"""
+  User Query: {user_query}
+  Table Metadata: {table_metadata}
+  """
+
+  return system_prompt,user_prompt
+
+
+
+def generate_output(system_prompt,user_prompt):
+  client = Groq(api_key=api,)
+  chat_completion = client.chat.completions.create(messages=[
+    {"role": "system", "content": system_prompt},
+     {"role": "user","content": user_prompt,}],model="llama3-70b-8192",)
+  res = chat_completion.choices[0].message.content
+
+  select=res[0:6].lower()
+  if(select=="select"):
+    output=res
+  else:
+    output="Can't perform the task at the moment."
+
+  return output
+
+
+def response(user_query):
+  embeddings,model,student,employee,course=create_metadata_embeddings()
+   
+  table_metadata=find_best_fit(embeddings,model,user_query,student,employee,course)
+
+  system_prompt,user_prompt=create_prompt(user_query,table_metadata)
+
+  output=generate_output(system_prompt,user_prompt)
+
+  return output
+
+
+demo = gr.Interface(
+    fn=response,
+    inputs=gr.Textbox(label="Please provide the natural language query"),
+    outputs=gr.Textbox(label="SQL Query"),
+    title="SQL Query generator"
+)
+
+demo.launch(share="True")