Update app.py

app.py

@@ -1,207 +1,207 @@
-from dotenv import load_dotenv
-import os
-from sentence_transformers import SentenceTransformer
-import gradio as gr
-from sklearn.metrics.pairwise import cosine_similarity
-from groq import Groq
-from database import initialize_database, run_sql_query
-
-load_dotenv()
-api = os.getenv("groq_api_key")
-
-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
-    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()
-#     return [student, employee, course][best_match_table]
-def find_best_fit(embeddings, model, user_query, metadata_list, table_names, top_k=2, threshold=0.4):
-    """
-    Identifies relevant tables for a query based on semantic similarity.
-    Returns a list of matching table metadata strings.
-    """
-    query_embedding = model.encode([user_query])
-    similarities = cosine_similarity(query_embedding, embeddings)[0]  # Flatten array
-
-    matched_tables = []
-    for idx, sim in enumerate(similarities):
-        if sim >= threshold:
-            matched_tables.append((table_names[idx], metadata_list[idx], sim))
-
-    # If none meet the threshold, use top-k highest scoring
-    if not matched_tables:
-        top_indices = similarities.argsort()[-top_k:][::-1]
-        matched_tables = [(table_names[i], metadata_list[i], similarities[i]) for i in top_indices]
-
-    return matched_tables  # List of (table_name, metadata, similarity)
-
-
-
-# def create_prompt(user_query, table_metadata):
-
-#     system_prompt = """
-#     You are an SQL query generator capable of handling multiple tables with relationships.
-
-# Use table metadata to construct accurate queries, including joins, based on the user's intent.
-
-# Ensure:
-# - The query is valid.
-# - All table and column names match metadata.
-# - Join conditions are based on matching keys (e.g., student_id = course.student_id).
-
-# Output a single-line SQL query only, without any explanation.
-
-#     """
-
-#     user_prompt = f"""
-#     User Query: {user_query}
-#     Table Metadata: {table_metadata}
-#     """
-#     return system_prompt, user_prompt
-def create_prompt(user_query, table_metadata):
-    system_prompt = """
-    You are a SQL query generator specialized in generating SQL queries for one or more tables. 
-    Your task is to convert natural language queries into SQL statements using the provided metadata.
-
-    Rules:
-    - Use JOINs only if required by user intent.
-    - Ensure the generated SQL query only uses the tables and columns mentioned in the metadata.
-    - Use standard SQL syntax in a single line.
-    - Do NOT explain or add comments — only return the SQL query string.
-
-    Input Format:
-    User Query: A natural language request.
-    Table Metadata: List of available tables and their structures.
-
-    Output Format:
-    SQL Query: A valid single-line SQL query only.
-    """
-
-    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
-    return res if res.lower().startswith("select") else "Can't perform the task at the moment."
-
-
-# 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)
-#     sql_query = generate_output(system_prompt, user_prompt)
-
-#     if sql_query.lower().startswith("select"):
-#         result = run_sql_query(sql_query)
-#         return f"SQL Query:\n{sql_query}\n\nResult:\n{result}"
-#     else:
-#         return sql_query
-def response(user_query):
-    embeddings, model, student, employee, course = create_metadata_embeddings()
-    metadata_list = [student, employee, course]
-    table_names = ["student", "employee", "course"]
-
-    matched_tables = find_best_fit(embeddings, model, user_query, metadata_list, table_names)
-
-    combined_metadata = "\n\n".join([table[1] for table in matched_tables])
-
-    system_prompt, user_prompt = create_prompt(user_query, combined_metadata)
-
-    sql_query = generate_output(system_prompt, user_prompt)
-
-    if sql_query.lower().startswith("select"):
-        result = run_sql_query(sql_query)
-        return f"SQL Query:\n{sql_query}\n\nResult:\n{result}"
-    else:
-        return f"SQL Query:\n{sql_query}\n\nResult:\nUnable to fetch data or unsupported query."
-
-
-
-
-
-# Initialize DB on app launch
-initialize_database()
-
-desc = """
-There are three tables in the database:
-
-Student Table:  
-Contains student ID, name, DOB, email, phone, major, year of enrollment.
-
-Employee Table:  
-Contains employee ID, name, email, department, position, salary, and joining date.
-
-Course Info Table:  
-Contains course ID, name, code, instructor ID, department, credits, and semester.
-"""
-
-import gradio as gr
-
-demo = gr.Interface(
-    fn=response,
-    inputs=gr.Textbox(label="Please provide the natural language query"),
-    outputs=gr.Textbox(label="SQL Query and Result"),
-    title="SQL Query Generator with Results",
-    description=desc
-)
-
-demo.launch(share=True)
-
+from dotenv import load_dotenv
+import os
+from sentence_transformers import SentenceTransformer
+import gradio as gr
+from sklearn.metrics.pairwise import cosine_similarity
+from groq import Groq
+from database import initialize_database, run_sql_query
+
+load_dotenv()
+api = os.getenv("groq_api_key")
+
+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
+    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 that refers to employee_id in the employee table (who teaches 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()
+#     return [student, employee, course][best_match_table]
+def find_best_fit(embeddings, model, user_query, metadata_list, table_names, top_k=2, threshold=0.4):
+    """
+    Identifies relevant tables for a query based on semantic similarity.
+    Returns a list of matching table metadata strings.
+    """
+    query_embedding = model.encode([user_query])
+    similarities = cosine_similarity(query_embedding, embeddings)[0]  # Flatten array
+
+    matched_tables = []
+    for idx, sim in enumerate(similarities):
+        if sim >= threshold:
+            matched_tables.append((table_names[idx], metadata_list[idx], sim))
+
+    # If none meet the threshold, use top-k highest scoring
+    if not matched_tables:
+        top_indices = similarities.argsort()[-top_k:][::-1]
+        matched_tables = [(table_names[i], metadata_list[i], similarities[i]) for i in top_indices]
+
+    return matched_tables  # List of (table_name, metadata, similarity)
+
+
+
+# def create_prompt(user_query, table_metadata):
+
+#     system_prompt = """
+#     You are an SQL query generator capable of handling multiple tables with relationships.
+
+# Use table metadata to construct accurate queries, including joins, based on the user's intent.
+
+# Ensure:
+# - The query is valid.
+# - All table and column names match metadata.
+# - Join conditions are based on matching keys (e.g., student_id = course.student_id).
+
+# Output a single-line SQL query only, without any explanation.
+
+#     """
+
+#     user_prompt = f"""
+#     User Query: {user_query}
+#     Table Metadata: {table_metadata}
+#     """
+#     return system_prompt, user_prompt
+def create_prompt(user_query, table_metadata):
+    system_prompt = """
+    You are a SQL query generator specialized in generating SQL queries for one or more tables. 
+    Your task is to convert natural language queries into SQL statements using the provided metadata.
+
+    Rules:
+    - Use JOINs only if required by user intent.
+    - Ensure the generated SQL query only uses the tables and columns mentioned in the metadata.
+    - Use standard SQL syntax in a single line.
+    - Do NOT explain or add comments — only return the SQL query string.
+
+    Input Format:
+    User Query: A natural language request.
+    Table Metadata: List of available tables and their structures.
+
+    Output Format:
+    SQL Query: A valid single-line SQL query only.
+    """
+
+    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
+    return res if res.lower().startswith("select") else "Can't perform the task at the moment."
+
+
+# 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)
+#     sql_query = generate_output(system_prompt, user_prompt)
+
+#     if sql_query.lower().startswith("select"):
+#         result = run_sql_query(sql_query)
+#         return f"SQL Query:\n{sql_query}\n\nResult:\n{result}"
+#     else:
+#         return sql_query
+def response(user_query):
+    embeddings, model, student, employee, course = create_metadata_embeddings()
+    metadata_list = [student, employee, course]
+    table_names = ["student", "employee", "course"]
+
+    matched_tables = find_best_fit(embeddings, model, user_query, metadata_list, table_names)
+
+    combined_metadata = "\n\n".join([table[1] for table in matched_tables])
+
+    system_prompt, user_prompt = create_prompt(user_query, combined_metadata)
+
+    sql_query = generate_output(system_prompt, user_prompt)
+
+    if sql_query.lower().startswith("select"):
+        result = run_sql_query(sql_query)
+        return f"SQL Query:\n{sql_query}\n\nResult:\n{result}"
+    else:
+        return f"SQL Query:\n{sql_query}\n\nResult:\nUnable to fetch data or unsupported query."
+
+
+
+
+
+# Initialize DB on app launch
+initialize_database()
+
+desc = """
+There are three tables in the database:
+
+Student Table:  
+Contains student ID, name, DOB, email, phone, major, year of enrollment.
+
+Employee Table:  
+Contains employee ID, name, email, department, position, salary, and joining date.
+
+Course Info Table:  
+Contains course ID, name, code, instructor ID, department, credits, and semester.
+"""
+
+import gradio as gr
+
+demo = gr.Interface(
+    fn=response,
+    inputs=gr.Textbox(label="Please provide the natural language query"),
+    outputs=gr.Textbox(label="SQL Query and Result"),
+    title="SQL Query Generator with Results",
+    description=desc
+)
+
+demo.launch(share=True)
+