feat: version 1

app.py

@@ -1,12 +1,12 @@
 import streamlit as st
 from llm import load_llm, response_generator
-from sql import csv_to_sqlite
+from sql import csv_to_sqlite, run_sql_query
 
 
-# repo_id = "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF"
-repo_id = "Qwen/Qwen2.5-0.5B-Instruct-GGUF"
-# filename="qwen2.5-coder-1.5b-instruct-q8_0.gguf"
-filename = "qwen2.5-0.5b-instruct-q8_0.gguf"
+repo_id = "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF"
+filename = "qwen2.5-coder-1.5b-instruct-q8_0.gguf"
+# repo_id = "Qwen/Qwen2.5-0.5B-Instruct-GGUF"
+# filename = "qwen2.5-0.5b-instruct-q8_0.gguf"
 
 llm = load_llm(repo_id, filename)
 
@@ -38,7 +38,12 @@ for message in st.session_state.messages:
         st.markdown(message["content"])
 
 # Accept user input
-if prompt := st.chat_input("What is up?"):
+if prompt := st.chat_input(
+    "What is up?",
+    disabled=(
+        not "db_name" in st.session_state or not "table_name" in st.session_state
+    ),
+):
     # Add user message to chat history
     st.session_state.messages.append({"role": "user", "content": prompt})
     # Display user message in chat message container
@@ -47,14 +52,17 @@ if prompt := st.chat_input("What is up?"):
 
     # Display assistant response in chat message container
     with st.chat_message("assistant"):
-        response = st.write(
-            response_generator(
-                db_name=st.session_state.db_name,
-                table_name=st.session_state.table_name,
-                llm=llm,
-                messages=st.session_state.messages,
-                question=prompt,
-            )
+        response_sql = response_generator(
+            db_name=st.session_state.db_name,
+            table_name=st.session_state.table_name,
+            llm=llm,
+            messages=st.session_state.messages,
+            question=prompt,
         )
+        response = st.markdown(response_sql)
+        result = run_sql_query(db_name=st.session_state.db_name, query=response_sql)
+        st.markdown(result)
+        st.table(result)
+
     # Add assistant response to chat history
-    st.session_state.messages.append({"role": "assistant", "content": response})
+    st.session_state.messages.append({"role": "assistant", "content": response_sql})

data/sales.csv

@@ -0,0 +1,11 @@
+Date,Product,Category,Quantity,Unit_Price,Total,Customer,Region
+2024-01-15,Laptop Pro X,Electronics,2,999.99,1999.98,John Smith,West
+2024-01-15,Wireless Mouse,Accessories,5,24.99,124.95,Maria Garcia,South
+2024-01-16,Desktop Monitor,Electronics,3,299.99,899.97,Robert Wilson,North
+2024-01-17,USB-C Cable,Accessories,10,12.99,129.90,Emily Chen,East
+2024-01-17,Laptop Pro X,Electronics,1,999.99,999.99,David Brown,West
+2024-01-18,Keyboard Elite,Accessories,4,89.99,359.96,Sarah Johnson,North
+2024-01-18,Webcam HD,Electronics,2,79.99,159.98,James Lee,South
+2024-01-19,Laptop Stand,Accessories,3,34.99,104.97,Lisa Anderson,East
+2024-01-19,Desktop Monitor,Electronics,2,299.99,599.98,Michael Wong,West
+2024-01-20,Wireless Mouse,Accessories,6,24.99,149.94,Rachel Martinez,North

llm.py

@@ -19,7 +19,7 @@ def load_llm(repo_id, filename):
     return llm
 
 
-def generate_llm_prompt(table_name, table_schema):
+def generate_system_prompt(table_name, table_schema):
     """
     Generates a prompt to provide context about a table's schema for LLM to convert natural language to SQL.
 
@@ -48,45 +48,27 @@ def generate_llm_prompt(table_name, table_schema):
     return prompt
 
 
-def generate_sql_query(question, table_name, db_name):
-    pass
-    # table_name = 'movies'
-    # db_name = 'movies_db.db'
-    # table_schema = get_table_schema(db_name, table_name)
-    # llm_prompt = generate_llm_prompt(table_name, table_schema)
-    # user_prompt = """Question: {question}"""
-    # response = completion(
-    #     api_key=OPENAI_API_KEY,
-    #     model="gpt-4o-mini",
-    #     messages=[
-    #         ,
-    #         {"content": user_prompt.format(question=question),"role": "user"}],
-    #     max_tokens=1000
-    # )
-    # answer = response.choices[0].message.content
-
-    # query = answer.replace("```sql", "").replace("```", "")
-    # query = query.strip()
-    # return query
-
-
 # Streamed response emulator
 def response_generator(llm, messages, question, table_name, db_name):
     table_schema = get_table_schema(db_name, table_name)
-    llm_prompt = generate_llm_prompt(table_name, table_schema)
-    user_prompt = """Question: {question}"""
+    llm_prompt = generate_system_prompt(table_name, table_schema)
+    user_prompt = f"""Question: {question}"""
 
-    messages = [{"content": llm_prompt.format(table_name=table_name), "role": "system"}]
+    print(messages, llm_prompt, user_prompt)
+    history = [{"content": llm_prompt.format(table_name=table_name), "role": "system"}]
 
-    for val in st.session_state.messages:
-        messages.append(val)
+    for val in messages:
+        history.append(val)
 
-    messages.append({"role": "user", "content": user_prompt})
+    history.append({"role": "user", "content": user_prompt})
 
     response = llm.create_chat_completion(
-        messages, max_tokens=2048, temperature=0.7, top_p=0.95
+        messages=history,
+        max_tokens=2048,
+        temperature=0.7,
+        top_p=0.95,
     )
-    answer = response["choices"][0].message.content
+    answer = response["choices"][0]["message"]["content"]
 
     query = answer.replace("```sql", "").replace("```", "")
     query = query.strip()