Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import random
+
+# Initialize the Hugging Face text generation pipeline with distilgpt2
+generator = pipeline("text-generation", model="distilgpt2")
+
+# Function to generate checklists, tips, and engagement score
+def generate_project_data(project_input):
+    # Generate checklists (3 tasks)
+    checklist_prompt = f"Generate a list of 3 safety and productivity tasks for a construction project: {project_input}"
+    checklist_response = generator(checklist_prompt, max_length=100, num_return_sequences=1, truncation=True)[0]["generated_text"]
+    # Extract tasks (simple parsing assuming the model returns a list-like structure)
+    tasks = checklist_response.replace(checklist_prompt, "").split(".")[:3]
+    tasks = [task.strip() for task in tasks if task.strip()]
+    if len(tasks) < 3:
+        # Fallback tasks if the model doesn't generate enough
+        tasks.extend([
+            "Conduct a safety briefing with the team.",
+            "Inspect all equipment before use.",
+            "Ensure all workers are wearing PPE."
+        ][:3 - len(tasks)])
+
+    # Generate a tip
+    tip_prompt = f"Provide a productivity tip for a construction project supervisor: {project_input}"
+    tip_response = generator(tip_prompt, max_length=50, num_return_sequences=1, truncation=True)[0]["generated_text"]
+    tip = tip_response.replace(tip_prompt, "").strip()
+    if not tip:
+        tip = "Schedule regular breaks to maintain team focus."
+
+    # Generate a mock engagement score (rule-based for simplicity)
+    # In a real scenario, this could be generated by a model trained on engagement data
+    engagement_score = random.randint(70, 90)  # Random score between 70 and 90
+
+    # Return the data in the expected JSON format
+    return {
+        "checklists": [{"task": task} for task in tasks],
+        "tips": tip,
+        "engagementScore": engagement_score
+    }
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=generate_project_data,
+    inputs=gr.Textbox(label="Project Input", placeholder="Enter project details (e.g., Project: Highway Construction, Start Date: 2025-05-01)"),
+    outputs=gr.JSON(label="Generated Data"),
+    title="AI Coach Data Generator",
+    description="Generates daily checklists, tips, and engagement scores for construction projects."
+)
+
+# Launch the app
+interface.launch()