Create app.py

app.py

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+# Install necessary libraries
+# pip install transformers datasets torch
+
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from transformers import Trainer, TrainingArguments
+from datasets import Dataset
+
+# Step 1: Load the pre-trained GPT-2 model and tokenizer
+model_name = "gpt2"  # You can use any GPT model, GPT-3, or other variants if you want a bigger model
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+
+# Set padding token as GPT-2 doesn't have one by default
+tokenizer.pad_token = tokenizer.eos_token
+
+# Step 2: Prepare your training data (Instagram algorithm and feature usage)
+training_data = [
+    {
+        "input": "How can I improve engagement on Instagram?",
+        "output": "Engagement can be improved by posting at optimal times, using 20-30 relevant hashtags, and responding to comments quickly. Consider using reels for higher visibility."
+    },
+    {
+        "input": "What are the best times to post on Instagram?",
+        "output": "The best times to post on Instagram depend on your audience's time zone. Typically, posting during peak activity times such as early morning or late evening can lead to better engagement."
+    },
+    {
+        "input": "How do I use Instagram Insights?",
+        "output": "Go to your profile, tap the menu, and select 'Insights.' You can view metrics like reach, impressions, and engagement."
+    },
+    {
+        "input": "What is the best way to use hashtags on Instagram?",
+        "output": "Use a mix of trending, niche, and brand-specific hashtags. Aim for around 20-30 relevant hashtags per post. Research the most effective ones for your target audience."
+    },
+    {
+        "input": "How can I use Instagram Stories to grow my account?",
+        "output": "Instagram Stories can be used to engage your followers by sharing behind-the-scenes content, polls, Q&As, and other interactive elements. Consistency and engaging content are key."
+    },
+]
+
+# Step 3: Process the data into a format suitable for training
+def process_data(examples):
+    # Concatenate input and output to form the training sequence
+    return tokenizer(examples['input'] + tokenizer.eos_token + examples['output'], truncation=True, padding="max_length", max_length=128)
+
+# Convert the training data into a dataset
+dataset = Dataset.from_dict(training_data)
+dataset = dataset.map(process_data, batched=True)
+
+# Step 4: Split the dataset into training and validation sets
+train_dataset = dataset.train_test_split(test_size=0.1)["train"]
+val_dataset = dataset.train_test_split(test_size=0.1)["test"]
+
+# Step 5: Define the training arguments
+training_args = TrainingArguments(
+    output_dir="./gpt2-instagram-model",     # Directory to save the model
+    evaluation_strategy="epoch",              # Evaluate at the end of each epoch
+    learning_rate=5e-5,                      # Learning rate for fine-tuning
+    per_device_train_batch_size=4,           # Batch size for training
+    per_device_eval_batch_size=4,            # Batch size for evaluation
+    num_train_epochs=3,                      # Number of training epochs
+    weight_decay=0.01,                       # Weight decay for regularization
+    logging_dir='./logs',                    # Log directory
+    logging_steps=200,                       # Log every 200 steps
+)
+
+# Step 6: Initialize the Trainer
+trainer = Trainer(
+    model=model,                             # The model we are training
+    args=training_args,                      # Training arguments
+    train_dataset=train_dataset,             # Training dataset
+    eval_dataset=val_dataset,                # Validation dataset
+)
+
+# Step 7: Train the model
+trainer.train()
+
+# Step 8: Evaluate the model after training
+results = trainer.evaluate()
+print("Evaluation Results:", results)
+
+# Step 9: Save the model and tokenizer
+model.save_pretrained("./gpt2-instagram-model")
+tokenizer.save_pretrained("./gpt2-instagram-model")
+
+# Step 10: Use the trained model to generate responses
+def generate_response(input_text):
+    # Encode the input text and generate a response
+    inputs = tokenizer.encode(input_text, return_tensors="pt")
+    output = model.generate(inputs, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2)
+    
+    # Decode and return the response
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+# Example: Generate a response
+input_text = "How can I improve engagement on Instagram?"
+response = generate_response(input_text)
+print("Generated Response:", response)