Update app.py

app.py

@@ -1,63 +1,73 @@
+import pandas as pd
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
-
-
-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})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import numpy as np
+from pandasai import SmartDataframe
+from pandasai.llm import OpenAI
+from PIL import Image
+import base64
+from io import BytesIO
+
+def ask(query, file, api_token):
+    data = pd.read_csv(file, index_col=0)
+    llm = OpenAI(api_token=api_token)
+    df = SmartDataframe(data, config={"llm": llm})
+    result = df.chat(query)
+    if isinstance(result, str) and result.endswith('.png'):
+        # Open the image file
+        img = Image.open('exports/charts/temp_chart.png')
+        
+        # Convert the PIL Image to a base64 encoded string
+        buffered = BytesIO()
+        img.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+        
+        # Construct the HTML string to display the image
+        html = f"<img src='data:image/png;base64,{img_str}' alt='Generated Image' style='width:100%;'>"
+        return html
+    elif isinstance(result, pd.DataFrame):
+        html_table = result.to_html()
+        return html_table
+    else:
+        return result
+
+def on_file_upload(file, api_token):
+    data = pd.read_csv(file, index_col=0)
+    llm = OpenAI(api_token=api_token)
+    df = SmartDataframe(data, config={"llm": llm})
+    result = df.chat('What are the name of my columns')
+    return result
+
+with gr.Blocks() as demo:
+    headers = gr.Markdown("<div style='text-align: center;'><h1>    This is a project from Chasers using Pandas AI</h1></div>")
+    
+    with gr.Row():
+        # First column for the first image
+        with gr.Column():
+            gr.HTML("""
+                <img src="https://d1b66s7evqera.cloudfront.net/f8d9b474-d1d2-4931-b38d-c5f918b8dc82/images/Chasers320x132.png?61554cbec993ea37a9d52399a0a0d3cd" width="320">
+            """)
+        
+        # Second column for the clickable image
+        with gr.Column():
+            gr.HTML("""
+                <a href='https://pandas-ai.com/' target='_blank'>
+                    <img src="https://framerusercontent.com/images/tDg1U6HZYYK3azrDbdVXLhPkOlk.png" width="150" style="opacity: 0.3";>
+                </a>
+            """)
+    
+    api_token_input = gr.Textbox(lines=1, label="Enter your API token")
+    file_input = gr.File() 
+    upload_button = gr.Button("Process Uploaded File")
+    upload_message = gr.Label("") 
+    upload_button.click(on_file_upload, inputs=[file_input, api_token_input], outputs=[upload_message])
+    query_input = gr.Textbox(lines=2, label="Use exactly the name of the columns in your querys")
+    output_html = gr.HTML()
+
+
+
+    # Button to trigger the ask function
+    submit_button = gr.Button("Submit")
+    submit_button.click(ask, inputs=[query_input, file_input, api_token_input], outputs=[output_html])
+
+demo.launch()
+