Update app.py

app.py

@@ -1,6 +1,3 @@
-# Install necessary packages (only for local environment)
-# !pip install pandas gradio transformers torch
-
 import pandas as pd
 import gradio as gr
 import torch
@@ -16,19 +13,29 @@ def load_model():
 model, tokenizer = load_model()
 
 def forecast(csv_file):
-    data = pd.read_csv(csv_file.name, parse_dates=['timestamp_column'])
-    
-    # Convert data to a format suitable for the model
-    input_text = data.to_json()
+    # Read CSV with correct delimiter and parse timestamps
+    data = pd.read_csv(csv_file.name, sep=";", parse_dates=['timestamp_column'])
+
+    # Ensure timestamp format is correct
+    data['timestamp_column'] = pd.to_datetime(data['timestamp_column'], format="%Y%m%d %H:%M")
+
+    # Convert data to a structured format for the model
+    input_text = "\n".join([f"{row['timestamp_column']}: {row['Inbound']}" for _, row in data.iterrows()])
     inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
-    
+
+    # Generate forecast
     with torch.no_grad():
-        predictions = model.generate(**inputs, max_length=200)
-    
+        predictions = model.generate(**inputs, max_length=200, num_return_sequences=1)
+
+    # Decode the generated forecast
     forecast_text = tokenizer.decode(predictions[0], skip_special_tokens=True)
+
+    # Save forecast result to CSV
     forecasts = pd.DataFrame({'forecast': [forecast_text]})
-    forecasts.to_csv("forecasts.csv", index=False)
-    return "forecasts.csv"
+    output_file = "forecasts.csv"
+    forecasts.to_csv(output_file, index=False)
+    
+    return output_file
 
 # Gradio Interface
 iface = gr.Interface(