Update app.py

app.py

@@ -1,63 +1,83 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import pandas as pd
+import matplotlib.pyplot as plt
+import io
+import sqlite3
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Initialize the InferenceClient with the specified model
+client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta")
 
+# Specify the path to your CSV file here
+csv_file_path = 'Movies.csv'
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
+# Load dataset into a dataframe
+df = pd.read_csv(csv_file_path)
 
+# Function to generate SQL queries
+def generate_sql_query(prompt, table_metadata):
+    input_text = f"Generate an SQL query for the table with the following structure: {table_metadata}. Prompt: {prompt}"
     response = ""
-
     for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
+        messages=[{"role": "system", "content": input_text}],
+        max_tokens=512,
         stream=True,
-        temperature=temperature,
-        top_p=top_p,
+        temperature=0.7,
+        top_p=0.95,
     ):
         token = message.choices[0].delta.content
-
         response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    return response
+
+# Function to execute SQL query on the dataframe
+def execute_query(df, query):
+    try:
+        with sqlite3.connect(':memory:') as conn:
+            df.to_sql('data', conn, index=False, if_exists='replace')
+            result_df = pd.read_sql_query(query, conn)
+        return result_df
+    except Exception as e:
+        return str(e)
+
+# Function to create a plot from the result dataframe
+def create_plot(df):
+    fig, ax = plt.subplots()
+    df.plot(ax=ax)
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    return buf
+
+# Gradio function to handle user input and interaction
+def respond(user_prompt, system_message, max_tokens, temperature, top_p):
+    table_metadata = str(df.dtypes.to_dict())
+    sql_query = generate_sql_query(user_prompt, table_metadata)
+    result_df = execute_query(df, sql_query)
+
+    if isinstance(result_df, str):
+        return sql_query, result_df, None  # Return the error message
+
+    plot = create_plot(result_df)
+    return sql_query, result_df.head().to_html(), plot
+
+# Gradio UI components
+def create_demo():
+    with gr.Blocks() as demo:
+        user_prompt = gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="User Prompt")
+        system_message = gr.Textbox(value="You are an AI assistant that generates SQL queries based on user prompts.", label="System message")
+        max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+        temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+        top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+        output_sql_query = gr.Textbox(label="Generated SQL Query")
+        output_result_df = gr.HTML(label="Query Result")
+        output_plot = gr.Image(label="Result Plot")
+
+        submit_btn = gr.Button("Submit")
+        submit_btn.click(respond, inputs=[user_prompt, system_message, max_tokens, temperature, top_p], outputs=[output_sql_query, output_result_df, output_plot])
 
+    return demo
 
 if __name__ == "__main__":
-    demo.launch()
+    demo = create_demo()
+    demo.launch()