Create app.py

app.py

@@ -0,0 +1,86 @@
+import streamlit as st
+from langchain import HuggingFaceHub
+from langchain.prompts import ChatPromptTemplate
+from langchain_google_genai import ChatGoogleGenerativeAI, HarmBlockThreshold, HarmCategory
+from langchain.chains.question_answering import load_qa_chain
+from dotenv import load_dotenv
+import os
+import json
+import pandas as pd
+import numpy as np
+import sqlite3
+
+
+load_dotenv()
+
+conn = sqlite3.connect('your_database.db')
+c = conn.cursor()
+
+
+def get_database_schema():
+    c.execute("SELECT name FROM sqlite_master WHERE type='table';")
+    tables = c.fetchall()
+    schema_dict = {}
+    for table in tables:
+        c.execute(f"PRAGMA table_info({table[0]})")
+        schema = c.fetchall()
+        schema_dict[table[0]] = schema
+    return schema_dict
+
+
+def get_chain():
+    template = """
+    You are a SQLite query generator.
+    return output executable query only or empyt text .
+    output should be json with key "query" and tabel name with key "table_name"
+    Based on user question, generate SQLite Query  for given database Schema.
+    Schema : \n{context}\n 
+    Question : {question}       
+    """
+    hf_model = HuggingFaceHub(repo_id="Mistralai/Mistralaichat", model_kwargs={"temperature": 0.4})
+    prompt=ChatPromptTemplate.from_template(template)
+    chain=LLMChain(llm=hf_model.load_chat(),prompt=prompt)
+    return chain
+
+def get_query(txt1):
+   start_index = txt1.find('{')
+   end_index = txt1.find('}') + 1
+   json_string = txt1[start_index:end_index]
+   data = json.loads(json_string)
+ 
+   return data
+
+
+def query_with_database(query, tablename=None):
+    if tablename:
+        c.execute(query)
+    else:
+        c.execute(query)
+    query_output = c.fetchall()
+    df = pd.DataFrame(query_output, columns=[desc[1] for desc in c.description])
+    st.table(df)
+
+
+def user_input(user_question):
+    schema = get_database_schema()
+    chain = get_chain()
+    response = chain.run({"context": schema, "question": user_question})
+    generated_query = get_query(response)
+    st.write("Generated Query : ", generated_query["query"])
+    query_with_database(generated_query["query"])
+
+
+def main():
+   st.header("Chat With SQLite DATABASE - HUGGING FACE MISTRALAI/MISTRALAICHAT")
+   with st.sidebar:
+       st.write("Develop by : Rehan Shekh")
+       st.write("Model : Hugging Face Mistralai/Mistralaichat")
+   buttoclick=st.text_input(label="Ask from Database")
+   st.write("Demo Database only have one table :- User_details")
+   if buttoclick:
+   
+      user_input(str(buttoclick))
+
+
+if __name__=="__main__":
+    main()