..

app.py

@@ -1,43 +1,42 @@
 # Install necessary packages (only for local environment)
-# !pip install pandas granite-tsfm gradio
+# !pip install pandas gradio transformers torch
 
 import pandas as pd
 import gradio as gr
-from granite-tsfm import TimeSeriesPreprocessor, TinyTimeMixerForPrediction, TimeSeriesForecastingPipeline
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the Hugging Face forecasting model
+def load_model():
+    model_name = "Ankur87/Llama2_Time_series_forecasting_7.0"  # Using the specified model
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+model, tokenizer = load_model()
 
-# Function to load model and make predictions
 def forecast(csv_file):
     data = pd.read_csv(csv_file.name, parse_dates=['timestamp_column'])
     
-    tsp = TimeSeriesPreprocessor(
-        id_columns=[],
-        timestamp_column='timestamp_column',
-        target_columns=['value1', 'value2'],  # Replace with your target column names
-        prediction_length=96,
-        context_length=512,
-        scaling=True
-    )
-    processed_data = tsp.fit_transform(data)
+    # Convert data to a format suitable for the model
+    input_text = data.to_json()
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
     
-    model = TinyTimeMixerForPrediction.from_pretrained(
-        'ibm-granite/granite-timeseries-ttm-r2',
-        num_input_channels=tsp.num_input_channels
-    )
+    with torch.no_grad():
+        predictions = model.generate(**inputs, max_length=200)
     
-    pipeline = TimeSeriesForecastingPipeline(
-        model=model,
-        feature_extractor=tsp
-    )
-    forecasts = pipeline(data)
-    return forecasts.to_csv(index=False)
+    forecast_text = tokenizer.decode(predictions[0], skip_special_tokens=True)
+    forecasts = pd.DataFrame({'forecast': [forecast_text]})
+    forecasts.to_csv("forecasts.csv", index=False)
+    return "forecasts.csv"
 
-# Create Gradio interface
+# Gradio Interface
 iface = gr.Interface(
     fn=forecast,
     inputs=gr.File(label="Upload CSV File"),
     outputs=gr.File(label="Download Forecasts"),
-    title="Time Series Forecasting with Granite-TimeSeries-TTM-R2",
-    description="Upload a CSV file with a timestamp column to generate forecasts."
+    title="Time Series Forecasting with Llama2",
+    description="Upload a CSV file with a timestamp column to generate forecasts using Llama2."
 )
 
 iface.launch()