Update app.py

app.py

@@ -1,17 +1,84 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
+# main.py
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
+import yfinance as yf
+import talib as ta
 import gradio as gr
+import pandas as pd
+import torch
 
-# Load a suitable LLM model (replace with the chosen model name)
-model_name = "databricks/dolly-v2-3b"  # Example: you can replace with a financial model
+# Step 1: Load Model and Tokenizer
+model_name = "databricks/dolly-v2-3b"  # Update with chosen model if different
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
+# Step 2: Financial Data Fetching and Indicators
+def fetch_stock_data(symbol="^KLSE"):
+    """Fetches financial data for a stock symbol."""
+    data = yf.download(symbol, start="2023-01-01", end="2023-10-30")
+    return data
+
+def calculate_macd(data):
+    """Calculates MACD for given stock data."""
+    macd, macd_signal, _ = ta.MACD(data['Close'])
+    return macd, macd_signal
+
+# Step 3: Fine-Tuning (Sample Training Setup, modify dataset path as needed)
+def fine_tune_model():
+    """Fine-tunes Dolly-v2 with custom financial data."""
+    # Example dataset, replace with actual financial dataset
+    dataset = pd.DataFrame({
+        "prompt": ["Explain KLCI's MACD trend.", "Predict KLCI based on SMA."],
+        "response": ["KLCI MACD shows bullish trend.", "KLCI SMA indicates resistance."]
+    })
+
+    # Tokenize the prompts and responses
+    inputs = tokenizer(dataset["prompt"].tolist(), padding=True, truncation=True, return_tensors="pt")
+    labels = tokenizer(dataset["response"].tolist(), padding=True, truncation=True, return_tensors="pt")["input_ids"]
+
+    # Training arguments
+    training_args = TrainingArguments(
+        output_dir="./results",
+        evaluation_strategy="epoch",
+        per_device_train_batch_size=2,
+        num_train_epochs=3,
+        weight_decay=0.01,
+    )
+
+    # Set up Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=inputs,
+    )
+    trainer.train()
+
+# Step 4: Response Generation with Dynamic Prompting
 def generate_response(prompt):
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs, max_length=100)
+    """Generates response using Dolly-v2 model with financial insights."""
+    data = fetch_stock_data()
+    macd, macd_signal = calculate_macd(data)
+    financial_prompt = f"The KLCI MACD value is {macd[-1]:.2f} with signal {macd_signal[-1]:.2f}. {prompt}"
+    
+    inputs = tokenizer(financial_prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=50)
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return response
 
-# Set up Gradio interface
-gr.Interface(fn=generate_response, inputs="text", outputs="text").launch()
+# Step 5: Deploying with Gradio Interface
+def build_interface():
+    """Sets up Gradio interface for user interaction."""
+    gr.Interface(
+        fn=generate_response, 
+        inputs="text", 
+        outputs="text"
+    ).launch(share=True)  # Set share=True for public link, if desired
+
+# Uncomment the below line to run fine-tuning when needed
+# fine_tune_model()
+
+# Run Gradio interface
+if __name__ == "__main__":
+    build_interface()
+