Update app.py

app.py

@@ -1,95 +1,30 @@
-import pandas as pd
 import gradio as gr
-from gpt4all import GPT4All
-import plotly.express as px
+import pandas as pd
+from transformers import pipeline
 
-# Initialize GPT4All model (adjust model path if needed)
-model = GPT4All("ggml-gpt4all-l13b-snoozy.bin")  
+# Load a small hosted model (no download needed)
+generator = pipeline("text-generation", model="mrm8488/t5-base-finetuned-e2m")
 
-def load_file(file):
-    # Read XLSX file
+def analyze_xlsx(file, prompt):
+    # Read the first few rows from the XLSX
     df = pd.read_excel(file.name)
-    cols = df.columns.tolist()
-    return df, cols, cols  # return dataframe + columns (for dropdowns)
+    preview = df.head(5).to_string(index=False)
+
+    # Combine prompt and table data
+    full_prompt = prompt + "\n\n" + preview
 
-def analyze_data(df, cols_selected):
-    if not cols_selected:
-        return "No columns selected.", None, None
-    
-    # Filter dataframe by selected columns
-    df_subset = df[cols_selected].head(10)
-    preview = df_subset.to_string()
-    
-    # Prepare prompt for AI
-    prompt = f"Analyze the following dataset snippet:\n{preview}\nProvide a concise summary of the data."
-    ai_response = model.generate(prompt)
-    
-    # Metadata summary: number of rows, missing values per selected column
-    metadata = {
-        'Row count': len(df),
-        'Selected columns': cols_selected,
-        'Missing values': df[cols_selected].isnull().sum().to_dict()
-    }
-    
-    # Create bar chart of missing values
-    missing_counts = df[cols_selected].isnull().sum()
-    fig = px.bar(
-        x=missing_counts.index,
-        y=missing_counts.values,
-        labels={'x': 'Columns', 'y': 'Missing Values'},
-        title="Missing Values per Selected Column"
-    )
-    fig.update_layout(xaxis_tickangle=-45)
-    
-    return ai_response, metadata, fig
+    # Generate AI response
+    result = generator(full_prompt, max_new_tokens=100, do_sample=True, temperature=0.7)
+    return result[0]['generated_text']
 
-with gr.Blocks() as demo:
-    gr.Markdown("# Advanced XLSX Analyzer with AI & Metadata Visualization")
-    
-    # Upload
-    file_input = gr.File(label="Upload XLSX file", file_types=['.xlsx'])
-    
-    # Placeholders for loaded data and columns
-    state_df = gr.State()
-    state_cols = gr.State()
-    
-    # Dropdown to select columns dynamically
-    cols_dropdown = gr.CheckboxGroup(label="Select Columns to Analyze")
-    
-    # Button to load columns after upload
-    load_button = gr.Button("Load Columns")
-    
-    # Textbox for AI analysis output
-    ai_output = gr.Textbox(label="AI Analysis Output", lines=10)
-    
-    # Metadata display (JSON)
-    meta_output = gr.JSON(label="Metadata Summary")
-    
-    # Plot output for missing values
-    plot_output = gr.Plot(label="Missing Values Chart")
-    
-    def load_columns(file):
-        df, cols1, cols2 = load_file(file)
-        return df, cols1, cols2
-    
-    def update_dropdown(df, cols):
-        # Update dropdown choices
-        return gr.update(choices=cols, value=cols)  # default all selected
-    
-    def run_analysis(df, selected_cols):
-        return analyze_data(df, selected_cols)
-    
-    # Upload triggers load file and update state
-    file_input.upload(load_columns, inputs=file_input, outputs=[state_df, state_cols, cols_dropdown])
-    
-    # Load columns button updates dropdown choices and selects all by default
-    load_button.click(update_dropdown, inputs=[state_df, state_cols], outputs=cols_dropdown)
-    
-    # Analyze on column selection change
-    cols_dropdown.change(run_analysis, inputs=[state_df, cols_dropdown], outputs=[ai_output, meta_output, plot_output])
-    
-    # Also analyze when clicking load_button (with default columns selected)
-    load_button.click(run_analysis, inputs=[state_df, cols_dropdown], outputs=[ai_output, meta_output, plot_output])
-    
-if __name__ == "__main__":
-    demo.launch()
+# Gradio interface
+gr.Interface(
+    fn=analyze_xlsx,
+    inputs=[
+        gr.File(file_types=[".xlsx"], label="Upload XLSX file"),
+        gr.Textbox(lines=2, placeholder="Enter your prompt (e.g. Summarize this data)", label="Your Prompt")
+    ],
+    outputs=gr.Textbox(label="AI Response"),
+    title="📊 XLSX AI Analyzer with Transformers",
+    description="Upload an Excel file and ask questions or request a summary. No API key or local model required.",
+).launch()