Create app.py

app.py

@@ -0,0 +1,109 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from joblib import Memory
+import hashlib
+import datetime
+
+# Initialize cache
+cache_dir = "./cache"
+memory = Memory(cache_dir, verbose=0)
+
+# Load fine-tuned model and tokenizer (replace with your model path)
+model_name = "distilgpt2"  # Placeholder; use your fine-tuned model from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Define prompt template
+PROMPT_TEMPLATE = """Role: {role}
+Project: {project_id}
+Milestones: {milestones}
+Reflection: {reflection}
+Generate a daily checklist, focus suggestions, and a motivational quote for a construction supervisor."""
+
+# Cache reset check
+last_reset = datetime.date.today()
+
+def reset_cache_if_new_day():
+    global last_reset
+    today = datetime.date.today()
+    if today > last_reset:
+        memory.clear()
+        last_reset = today
+
+# Cached generation function
+@memory.cache
+def generate_outputs(role, project_id, milestones, reflection):
+    reset_cache_if_new_day()
+    
+    # Validate inputs
+    if not all([role, project_id, milestones, reflection]):
+        return "Error: All fields are required."
+    
+    # Create prompt
+    prompt = PROMPT_TEMPLATE.format(
+        role=role,
+        project_id=project_id,
+        milestones=milestones,
+        reflection=reflection
+    )
+    
+    # Tokenize and generate
+    inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(
+        inputs["input_ids"],
+        max_length=1000,
+        num_return_sequences=1,
+        no_repeat_ngram_size=2,
+        do_sample=True,
+        top_p=0.9,
+        temperature=0.7
+    )
+    
+    # Decode and parse output
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    sections = generated_text.split("\n\n")
+    
+    checklist = sections[0] if len(sections) > 0 else "No checklist generated."
+    suggestions = sections[1] if len(sections) > 1 else "No suggestions generated."
+    quote = sections[2] if len(sections) > 2 else "No quote generated."
+    
+    return checklist, suggestions, quote
+
+# Gradio interface
+def create_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Construction Supervisor AI Coach")
+        gr.Markdown("Enter details to generate a daily checklist, focus suggestions, and a motivational quote.")
+        
+        with gr.Row():
+            role = gr.Dropdown(choices=["Supervisor", "Foreman", "Project Manager"], label="Role")
+            project_id = gr.Textbox(label="Project ID")
+        
+        milestones = gr.Textbox(label="Milestones (comma-separated KPIs)")
+        reflection = gr.Textbox(label="Reflection Log", lines=5)
+        
+        with gr.Row():
+            submit = gr.Button("Generate")
+            clear = gr.Button("Clear")
+        
+        checklist_output = gr.Textbox(label="Daily Checklist")
+        suggestions_output = gr.Textbox(label="Focus Suggestions")
+        quote_output = gr.Textbox(label="Motivational Quote")
+        
+        submit.click(
+            fn=generate_outputs,
+            inputs=[role, project_id, milestones, reflection],
+            outputs=[checklist_output, suggestions_output, quote_output]
+        )
+        clear.click(
+            fn=lambda: ("", "", "", ""),
+            inputs=None,
+            outputs=[role, project_id, milestones, reflection]
+        )
+    
+    return demo
+
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()