Rename test_local.py to fine_tune.py

fine_tune.py

@@ -0,0 +1,91 @@
+# Fine-tuning on Hugging Face Notebook
+# This runs in a HF notebook with GPU
+
+# Install requirements
+!pip install transformers datasets pandas scikit-learn accelerate
+
+import pandas as pd
+from transformers import (
+    AutoTokenizer, 
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainingArguments,
+    Seq2SeqTrainer
+)
+from datasets import Dataset
+from sklearn.model_selection import train_test_split
+
+# Load your dataset
+df = pd.read_csv('Data_1.csv')
+print(f"Dataset loaded: {len(df)} rows")
+
+# Data preprocessing
+df = df.dropna()
+train_df, val_df = train_test_split(df, test_size=0.2, random_state=42)
+
+print(f"Training samples: {len(train_df)}")
+print(f"Validation samples: {len(val_df)}")
+
+# Load model and tokenizer
+model_name = "Helsinki-NLP/opus-mt-en-fr"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Preprocessing function
+def preprocess_function(examples):
+    inputs = []
+    for i in range(len(examples['year'])):
+        input_text = f"Generate French title: Year: {examples['year'][i]} City: {examples['city'][i]} Event: {examples['original_title'][i]}"
+        inputs.append(input_text)
+    
+    targets = examples['target_title']
+    
+    model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding=True)
+    with tokenizer.as_target_tokenizer():
+        labels = tokenizer(targets, max_length=128, truncation=True, padding=True)
+    
+    model_inputs["labels"] = labels["input_ids"]
+    return model_inputs
+
+# Prepare datasets
+train_dataset = Dataset.from_pandas(train_df)
+val_dataset = Dataset.from_pandas(val_df)
+
+tokenized_train = train_dataset.map(preprocess_function, batched=True)
+tokenized_val = val_dataset.map(preprocess_function, batched=True)
+
+# Training arguments
+training_args = Seq2SeqTrainingArguments(
+    output_dir="./french-title-model",
+    evaluation_strategy="epoch",
+    learning_rate=5e-5,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    weight_decay=0.01,
+    save_total_limit=3,
+    num_train_epochs=10,
+    predict_with_generate=True,
+    push_to_hub=True,
+    hub_model_id="sufya12/french-title-model",  # Change to your username
+)
+
+# Data collator
+data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+
+# Trainer
+trainer = Seq2SeqTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_train,
+    eval_dataset=tokenized_val,
+    tokenizer=tokenizer,
+    data_collator=data_collator,
+)
+
+# Start training
+print("Starting training...")
+trainer.train()
+
+# Save and push to Hub
+trainer.push_to_hub()
+print("Training completed and model pushed to Hugging Face Hub!")

test_local.py

@@ -1,20 +0,0 @@
-import requests
-import json
-
-def test_local_api():
-    url = "http://localhost:7860/api/predict/"
-    
-    test_data = {
-        "data": ["2025", "Paris", "Jazz Festival in the City Park", 0.8, 50]
-    }
-    
-    try:
-        response = requests.post(url, json=test_data)
-        print("Status Code:", response.status_code)
-        print("Response:", response.json())
-    except Exception as e:
-        print("Error:", e)
-        print("Make sure your app is running on port 7860!")
-
-if __name__ == "__main__":
-    test_local_api()