Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import datetime
+
+# Initialize model and tokenizer (preloading them for quicker response)
+model_name = "distilgpt2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Set pad_token_id to eos_token_id to avoid warnings
+tokenizer.pad_token = tokenizer.eos_token
+model.config.pad_token_id = tokenizer.eos_token_id
+
+# Define a more contextual prompt template
+PROMPT_TEMPLATE = """You are an AI coach for construction supervisors. Based on the following inputs, generate a daily checklist, focus suggestions, and a motivational quote. Format your response with clear labels as follows:
+
+Checklist:
+- {milestones_list}
+
+Suggestions:
+- {suggestions_list}
+
+Quote:
+- Your motivational quote here
+
+Inputs:
+Role: {role}
+Project: {project_id}
+Milestones: {milestones}
+Reflection: {reflection}
+"""
+
+# Function to generate outputs based on inputs
+def generate_outputs(role, project_id, milestones, reflection):
+    # Validate inputs to ensure no missing fields
+    if not all([role, project_id, milestones, reflection]):
+        return "Error: All fields are required.", "", ""
+    
+    # Create prompt from template
+    milestones_list = "\n- ".join([m.strip() for m in milestones.split(",")])
+    
+    suggestions_list = ""
+    if "delays" in reflection.lower():
+        suggestions_list = "- Consider adjusting timelines to accommodate delays.\n- Communicate delays to all relevant stakeholders."
+    elif "weather" in reflection.lower():
+        suggestions_list = "- Ensure team has rain gear.\n- Monitor weather updates for possible further delays."
+    elif "equipment" in reflection.lower():
+        suggestions_list = "- Inspect all equipment to ensure no malfunctions.\n- Schedule maintenance if necessary."
+
+    # Create final prompt
+    prompt = PROMPT_TEMPLATE.format(
+        role=role,
+        project_id=project_id,
+        milestones=milestones,
+        reflection=reflection,
+        milestones_list=milestones_list,
+        suggestions_list=suggestions_list
+    )
+
+    # Tokenize inputs for model processing
+    inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True, padding=True)
+    
+    # Generate response from the model
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs['input_ids'],
+            max_length=512,
+            num_return_sequences=1,
+            no_repeat_ngram_size=2,
+            do_sample=True,
+            top_p=0.9,
+            temperature=0.8,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    # Decode the response
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Parse the output and ensure it is structured
+    checklist = "No checklist generated."
+    suggestions = "No suggestions generated."
+    quote = "No quote generated."
+
+    if "Checklist:" in generated_text:
+        checklist_start = generated_text.find("Checklist:") + len("Checklist:")
+        suggestions_start = generated_text.find("Suggestions:")
+        checklist = generated_text[checklist_start:suggestions_start].strip()
+    
+    if "Suggestions:" in generated_text:
+        suggestions_start = generated_text.find("Suggestions:") + len("Suggestions:")
+        quote_start = generated_text.find("Quote:")
+        suggestions = generated_text[suggestions_start:quote_start].strip()
+    
+    if "Quote:" in generated_text:
+        quote_start = generated_text.find("Quote:") + len("Quote:")
+        quote = generated_text[quote_start:].strip()
+
+    # Return structured outputs
+    return checklist, suggestions, quote
+
+# Gradio interface for fast user interaction
+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()