Upload 3 files

app (1).py

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+import gradio as gr
+import pickle
+
+def load_and_display_data():
+    """Loads the pickled data and formats it for display."""
+    try:
+        with open('canteen_surplus_data.pkl', 'rb') as f:
+            data = pickle.load(f)
+        # Format the dictionary for better display in Gradio
+        formatted_output = ""
+        for canteen_id, info in data.items():
+            formatted_output += f"Canteen ID: {canteen_id}\n"
+            formatted_output += f"  Canteen Name: {info['canteen_name']}\n"
+            formatted_output += f"  Predicted Surplus: {info['predicted_surplus']}\n"
+            formatted_output += f"  NGO Requirement: {info['ngo_requirement']}\n"
+            formatted_output += "-" * 20 + "\n"
+        return formatted_output
+    except FileNotFoundError:
+        return "Error: canteen_surplus_data.pkl not found."
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+# Create a Gradio interface to display the data
+iface = gr.Interface(
+    fn=load_and_display_data,
+    inputs=None,  # No input needed to display the data
+    outputs="text",
+    title="Canteen Surplus and NGO Requirements",
+    description="Displays the predicted surplus units and NGO requirements for each canteen."
+)
+
+iface.launch()

canteen_surplus_data.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f8f08a8b87d17a802bbf3d7464c89259a6f404a6564737767e6c44eeafe8177d
+size 518

requirements (1).txt

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+pandas
+scikit-learn
+catboost
+xgboost
+matplotlib
+seaborn
+huggingface_hub
+joblib