Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from groq import Groq
+
+# Initialize Groq API
+client = Groq(api_key="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")  # Replace with your Groq API key
+
+# Function to infer resources based on task types
+def infer_resources(schedule):
+    resource_map = {
+        "Excavation": {"labor": 10, "equipment": "Excavator", "material": "Soil"},
+        "Foundation": {"labor": 15, "equipment": "Concrete Mixer", "material": "Concrete"},
+        "Framing": {"labor": 20, "equipment": "Cranes", "material": "Steel"},
+        "Finishing": {"labor": 5, "equipment": "Hand Tools", "material": "Paint"}
+    }
+
+    inferred_resources = []
+    for _, row in schedule.iterrows():
+        task = row["task"]
+        resources = resource_map.get(task, {"labor": 5, "equipment": "General", "material": "Standard"})
+        inferred_resources.append({
+            "task": task,
+            "labor": resources["labor"],
+            "equipment": resources["equipment"],
+            "material": resources["material"]
+        })
+
+    return pd.DataFrame(inferred_resources)
+
+# Mock optimization logic
+def mock_optimize_schedule(schedule_with_resources):
+    # Simulate a simple optimization process
+    optimized_schedule = []
+    conflicts = []
+
+    for _, row in schedule_with_resources.iterrows():
+        task = row["task"]
+        start_date = row["start_date"]
+        end_date = row["end_date"]
+        labor = row["labor"]
+        equipment = row["equipment"]
+        material = row["material"]
+
+        # Check for conflicts (mock logic)
+        if labor > 20:  # Example conflict condition
+            conflicts.append(f"Task '{task}' exceeds labor capacity.")
+        
+        optimized_schedule.append({
+            "task": task,
+            "start_date": start_date,
+            "end_date": end_date,
+            "labor": labor,
+            "equipment": equipment,
+            "material": material,
+            "conflict": "Yes" if f"Task '{task}' exceeds labor capacity." in conflicts else "No"
+        })
+
+    return pd.DataFrame(optimized_schedule), conflicts
+
+# Main function for resource optimization
+def optimize_resources(schedule_file):
+    try:
+        # Load schedule file
+        schedule = pd.read_csv(schedule_file.name)
+
+        # Infer resources
+        inferred_resources = infer_resources(schedule)
+        schedule_with_resources = pd.concat([schedule, inferred_resources], axis=1)
+
+        # Perform optimization (mocked for now)
+        optimized_schedule_df, conflicts = mock_optimize_schedule(schedule_with_resources)
+
+        return optimized_schedule_df, "\n".join(conflicts) if conflicts else "No conflicts detected."
+    except Exception as e:
+        return f"Error: {e}"
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=optimize_resources,
+    inputs=[
+        gr.File(label="Upload Schedule File (CSV)")
+    ],
+    outputs=[
+        gr.Dataframe(label="Optimized Schedule"),  # Tabular output
+        gr.Textbox(label="Conflicts")  # Text output for conflict details
+    ],
+    title="Intelligent Resource Loading",
+    description="Upload a construction schedule to generate an optimized schedule and resource allocation description. Resources are inferred automatically."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()