Update app.py

app.py

@@ -1,60 +1,50 @@
 import pandas as pd
-import gradio as gr
-from transformers import pipeline
-
-# Load the already uploaded BANKNIFTY option chain data
-file_path = '/mnt/data/BANKNIFTY_OPTION_CHAIN_data.csv'
-
-def load_data():
-    try:
-        # Load the CSV file from the file path
-        data = pd.read_csv(file_path)
-        return data
-    except Exception as e:
-        return f"Error loading data: {e}"
-
-# Analyze the data using a Hugging Face model
-def analyze_data(question, model_name="impira/layoutlm-document-qa"):
-    # Load the data
-    data = load_data()
-    if isinstance(data, str):  # If error string is returned
-        return data
-    
-    # Load the Hugging Face LayoutLM pipeline for document question answering
-    try:
-        question_answerer = pipeline("document-question-answering", model=model_name)
-        
-        # Convert DataFrame to JSON-like format for the model
-        context = data.to_json(orient='records')
-        
-        # Ask the question and get the answer
-        result = question_answerer(question=question, context=context)
-        return result
-    except Exception as e:
-        return f"Error analyzing data: {e}"
-
-# Gradio interface definition
-def gradio_interface():
-    # Define input fields
-    question_input = gr.inputs.Textbox(lines=2, placeholder="Ask a question about the data", label="Question")
-
-    # Output field
-    output_text = gr.outputs.Textbox(label="Answer")
-
-    # Create the Gradio Interface
-    interface = gr.Interface(
-        fn=analyze_data,
-        inputs=question_input,
-        outputs=output_text,
-        title="BANKNIFTY Option Chain Analyzer",
-        description="Ask a question to extract insights from the BANKNIFTY option chain data.",
-        allow_flagging="never"
-    )
-
-    return interface
-
-# Launch Gradio app
-if __name__ == "__main__":
-    # Start the Gradio interface
-    app = gradio_interface()
-    app.launch()
+from transformers import LLaMAForSequenceClassification, LLaMATokenizer
+
+# Load the data
+data = pd.read_csv('BANKNIFTY_OPTION_CHAIN_data.csv')
+
+# Preprocess the data
+tokenizer = LLaMATokenizer.from_pretrained('llama-2-7b')
+model = LLaMAForSequenceClassification.from_pretrained('llama-2-7b', num_labels=2)
+
+# Fine-tune the model on the dataset
+train_texts, val_texts, train_labels, val_labels = train_test_split(data['text'], data['label'], test_size=0.2, random_state=42)
+
+train_encodings = tokenizer(train_texts, truncation=True, padding=True)
+val_encodings = tokenizer(val_texts, truncation=True, padding=True)
+
+train_dataset = Dataset(train_encodings, train_labels)
+val_dataset = Dataset(val_encodings, val_labels)
+
+training_args = TrainingArguments(
+    output_dir='./results',          # output directory
+    num_train_epochs=3,              # total # of training epochs
+    per_device_train_batch_size=16,  # batch size per device during training
+    per_device_eval_batch_size=64,   # batch size for evaluation
+    warmup_steps=500,                # number of warmup steps for learning rate scheduler
+    weight_decay=0.01,               # strength of weight decay
+    logging_dir='./logs',            # directory for storing logs
+)
+
+trainer = Trainer(
+    model=model,                         # the instantiated model
+    args=training_args,                  # training arguments
+    train_dataset=train_dataset,         # training dataset
+    eval_dataset=val_dataset             # evaluation dataset
+)
+
+trainer.train()
+
+# Use the fine-tuned model to generate strategies
+def generate_strategies(data):
+    inputs = tokenizer(data['text'], return_tensors='pt')
+    outputs = model(**inputs)
+    logits = outputs.logits
+    strategies = torch.argmax(logits, dim=1)
+    return strategies
+
+strategies = generate_strategies(data)
+
+# Print the strategies
+print(strategies)