changes

app.py

@@ -1,50 +1,59 @@
 import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
 import matplotlib.pyplot as plt
-from transformers import pipeline
-
-# Load the Hugging Face pipeline for translation
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
-
-# Load the Hugging Face pipeline for text generation using GPT-J
-generator = pipeline("text-generation", model="EleutherAI/gpt-j-6B")
-
-# Define the function to generate the graph based on the translated prompt
-def generate_graph(prompt):
-    # Translate the prompt to the desired language (e.g., from English to Spanish)
-    translated_prompt = translator(prompt, max_length=100, src_lang="en", tgt_lang="es")
-    translated_text = translated_prompt[0]['translation_text']
-
-    # Generate text using the Hugging Face pipeline with GPT-J
-    response = generator(translated_text, max_length=100)
-    text = response[0]['generated_text']
-
-    # Generate the graph using Matplotlib
-    # Replace this code with your specific graph generation logic
-    x = [1, 2, 3, 4, 5]
-    y = [2, 4, 6, 8, 10]
-    plt.plot(x, y)
-    plt.xlabel('X')
-    plt.ylabel('Y')
-    plt.title('Generated Graph')
-
-    # Save the generated graph to a file
-    graph_path = '/path/to/generated_graph.png'
-    plt.savefig(graph_path)
-
-    return graph_path
-
-# Create the Gradio interface
+import io
+
+def linear_regression(input_csv, x_column, y_column):
+    # Load dataset
+    df = pd.read_csv(input_csv)
+    
+    # Prepare data for regression
+    X = df[[x_column]].values
+    y = df[y_column].values
+
+    # Perform linear regression
+    model = LinearRegression()
+    model.fit(X, y)
+
+    # Make predictions
+    y_pred = model.predict(X)
+
+    # Plotting
+    plt.figure(figsize=(10, 6))
+    plt.scatter(X, y, color='blue')
+    plt.plot(X, y_pred, color='red')
+    plt.xlabel(x_column)
+    plt.ylabel(y_column)
+    plt.title('Linear Regression')
+
+    # Save plot to a buffer
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+
+    # Regression info
+    coef_info = f"Coefficient: {model.coef_[0]}\nIntercept: {model.intercept_}"
+
+    return buf, coef_info
+
+# Gradio interface
 iface = gr.Interface(
-    fn=generate_graph,
-    inputs="text",
-    outputs="file",
-    title="Graph Generator",
-    description="Generate a graph based on a translated prompt",
-    examples=[
-        ["Translate and generate a graph"],
-        ["Translate and graph the relationship between X and Y"],
-    ]
+    fn=linear_regression,
+    inputs=[
+        gr.inputs.File(type="csv"),
+        gr.inputs.Textbox(label="X Column Name"),
+        gr.inputs.Textbox(label="Y Column Name"),
+    ],
+    outputs=[
+        gr.outputs.Image(type="plot"),
+        gr.outputs.Textbox(label="Regression Info")
+    ],
+    title="Automatic Linear Regression Modeling",
+    description="Upload a CSV file and specify the columns for performing linear regression."
 )
 
-# Launch the Gradio interface
-iface.launch()
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -1,6 +1,5 @@
 gradio
-matplotlib
-transformers
-pytorch
-tensorflow
-torch
+pandas
+numpy
+scikit-learn
+matplotlib