app.py creation

app.py

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+import gradio as gr
+import pandas as pd
+import requests
+from transformers import GPT2LMHeadModel, GPT2Tokenizer, LlamaTokenizer, LlamaForCausalLM, pipeline
+from huggingface_hub import HfFolder, login
+from io import StringIO
+
+# Load GPT-2 model and tokenizer
+tokenizer_gpt2 = GPT2Tokenizer.from_pretrained('gpt2')
+model_gpt2 = GPT2LMHeadModel.from_pretrained('gpt2')
+
+# Create a pipeline for text generation using GPT-2
+text_generator = pipeline("text-generation", model=model_gpt2)
+
+# Load the LLaMA tokenizer
+tokenizer_llama = LlamaTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B")
+
+# Define your prompt template
+prompt_template = """\
+You are an expert in generating synthetic data for machine learning models.
+
+Your task is to generate a synthetic tabular dataset based on the description provided below.
+
+Description: {description}
+
+The dataset should include the following columns: {columns}
+
+Please provide the data in CSV format with a minimum of 100 rows per generation.
+Ensure that the data is realistic, does not contain any duplicate rows, and follows any specific conditions mentioned.
+
+Example Description:
+Generate a dataset for predicting house prices with columns: 'Size', 'Location', 'Number of Bedrooms', 'Price'
+
+Example Output:
+Size,Location,Number of Bedrooms,Price
+1200,Suburban,3,250000
+900,Urban,2,200000
+1500,Rural,4,300000
+...
+
+Description:
+{description}
+Columns:
+{columns}
+Output: """
+
+def preprocess_user_prompt(user_prompt):
+    generated_text = text_generator(user_prompt, max_length=50, num_return_sequences=1)[0]["generated_text"]
+    return generated_text
+
+def format_prompt(description, columns):
+    processed_description = preprocess_user_prompt(description)
+    prompt = prompt_template.format(description=processed_description, columns=",".join(columns))
+    return prompt
+
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3.1-8B"
+
+generation_params = {
+    "top_p": 0.90,
+    "temperature": 0.8,
+    "max_new_tokens": 512,
+    "return_full_text": False,
+    "use_cache": False
+}
+
+def generate_synthetic_data(description, columns):
+    formatted_prompt = format_prompt(description, columns)
+    payload = {"inputs": formatted_prompt, "parameters": generation_params}
+    headers = {"Authorization": f"Bearer {HfFolder.get_token()}"}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    
+    if response.status_code == 200:
+        response_json = response.json()
+        if isinstance(response_json, list) and len(response_json) > 0 and "generated_text" in response_json[0]:
+            return response_json[0]["generated_text"]
+        else:
+            raise ValueError("Unexpected response format or missing 'generated_text' key")
+    else:
+        raise ValueError(f"API request failed with status code {response.status_code}: {response.text}")
+
+def generate_large_synthetic_data(description, columns, num_rows=1000, rows_per_generation=100):
+    data_frames = []
+    num_iterations = num_rows // rows_per_generation
+
+    for _ in range(num_iterations):
+        generated_data = generate_synthetic_data(description, columns)
+        df_synthetic = process_generated_data(generated_data)
+        data_frames.append(df_synthetic)
+    
+    return pd.concat(data_frames, ignore_index=True)
+
+def process_generated_data(csv_data):
+    data = StringIO(csv_data)
+    df = pd.read_csv(data)
+    return df
+
+def main(description, columns):
+    description = description.strip()
+    columns = [col.strip() for col in columns.split(',')]
+    df_synthetic = generate_large_synthetic_data(description, columns)
+    return df_synthetic.to_csv(index=False)
+
+# Gradio interface
+iface = gr.Interface(
+    fn=main,
+    inputs=[
+        gr.Textbox(label="Description", placeholder="e.g., Generate a dataset for predicting students' grades"),
+        gr.Textbox(label="Columns (comma-separated)", placeholder="e.g., name, age, course, grade")
+    ],
+    outputs="text",
+    title="Synthetic Data Generator",
+    description="Generate synthetic tabular datasets based on a description and specified columns."
+)
+
+# Run the Gradio app
+iface.launch()