Create interface.py

interface.py

@@ -0,0 +1,74 @@
+# interface.py
+
+from UI import create_interface
+from models import BioprocessModel
+import io
+from PIL import Image
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import copy
+from config import DEVICE, MODEL_PATH, MAX_LENGTH, TEMPERATURE
+
+device = DEVICE
+model_path = MODEL_PATH
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path).to(device).eval()
+
+def generate_analysis(prompt, max_length=MAX_LENGTH):
+    try:
+        input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
+        generated_ids = model.generate(
+            input_ids=input_ids,
+            max_length=max_length + len(input_ids[0]),
+            temperature=TEMPERATURE,
+            num_return_sequences=1,
+            no_repeat_ngram_size=2,
+            early_stopping=True
+        )
+        output_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        analysis = output_text[len(prompt):].strip()
+        return analysis
+    except Exception as e:
+        return f"An error occurred during analysis: {e}"
+
+def parse_bounds(bounds_str, num_params):
+    try:
+        bounds = eval(f"[{bounds_str}]")
+        if len(bounds) != num_params:
+            raise ValueError
+        lower_bounds = [b[0] for b in bounds]
+        upper_bounds = [b[1] for b in bounds]
+        return lower_bounds, upper_bounds
+    except:
+        lower_bounds = [-np.inf] * num_params
+        upper_bounds = [np.inf] * num_params
+        return lower_bounds, upper_bounds
+
+def process_and_plot(
+    file,
+    biomass_equations_list,
+    biomass_params_list,
+    biomass_bounds_list,
+    substrate_equations_list,
+    substrate_params_list,
+    substrate_bounds_list,
+    product_equations_list,
+    product_params_list,
+    product_bounds_list,
+    legend_position,
+    show_legend,
+    show_params,
+    biomass_eq_count,
+    substrate_eq_count,
+    product_eq_count
+):
+    # Implement the function to process data, fit models, generate plots, and get analysis
+    # Return the plot image and analysis
+    return [image], analysis
+
+if __name__ == "__main__":
+    demo = create_interface(process_and_plot)
+    demo.launch()