Files uploaded

app.py

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+import gradio as gr
+import pandas as pd
+from prophet import Prophet
+import json
+import os
+import google.generativeai as genai 
+
+try:
+    api_key = os.environ.get("GOOGLE_API_KEY")
+    genai.configure(api_key=api_key)
+    print("Google AI API key configured successfully!")
+    LLM_LOADED = True
+    model = genai.GenerativeModel('gemini-pro')
+except Exception as e:
+    print(f"Could not configure Google AI. AI features will be disabled. Error: {e}")
+    LLM_LOADED = False
+    model = None
+
+
+def create_cases_data():
+    """Creates a dummy CSV with customer case data."""
+    cases_data = {
+        'case_id': [101, 102],
+        'customer_name': ['Rohan Gupta', 'Priya Sharma'],
+        'customer_query': [
+            "The Leather care conditioner 500 ml I ordered (Order #404-7654321-1234567) just arrived, but the bottle is leaking. I want a replacement.",
+            "Hi, my order for the Cockpit Cleaner Matt 500 ml (Order #404-1234567-9876543) was supposed to be delivered yesterday, but the tracking says it's delayed. Where is it?"
+        ],
+        'order_number': ['404-7654321-1234567', '404-1234567-9876543'],
+        'item_name': ['Leather care conditioner 500 ml', 'Cockpit Cleaner Matt 500 ml'],
+        'order_status': ['Delivered', 'Shipped']
+    }
+    df_cases = pd.DataFrame(cases_data)
+    df_cases.to_csv('customer_cases.csv', index=False)
+
+create_cases_data()
+
+# === USE CASE 1: Future Sales Prediction ===
+def predict_future_sales(sku_choice):
+    if not sku_choice:
+        return "Please select a SKU."
+    
+    df_sales = pd.read_csv('dummy_sales_history.csv')
+    df_sku = df_sales[df_sales['sku'] == sku_choice]
+
+    if df_sku.empty:
+        return f"No sales data could be found for '{sku_choice}'. Please check the CSV file."
+
+    df_sku = df_sku.rename(columns={'date': 'ds', 'units_sold': 'y'})
+    df_sku['ds'] = pd.to_datetime(df_sku['ds'])
+
+    model_prophet = Prophet(daily_seasonality=False, weekly_seasonality=True, yearly_seasonality=True)
+    model_prophet.fit(df_sku)
+    future = model_prophet.make_future_dataframe(periods=30)
+    forecast = model_prophet.predict(future)
+
+    thirty_day_forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(30)
+    total_sales = int(thirty_day_forecast['yhat'].sum())
+    lower_bound = int(thirty_day_forecast['yhat_lower'].sum())
+    upper_bound = int(thirty_day_forecast['yhat_upper'].sum())
+
+    return f"Predicted sales for **{sku_choice}** in the next 30 days: **{total_sales} units**.\nPrediction Range: *{lower_bound} to {upper_bound} units*."
+
+# === USE CASE 2: Automated Case Reply Generation ===
+def get_open_cases():
+    if not os.path.exists('customer_cases.csv'):
+        return []
+    df_cases = pd.read_csv('customer_cases.csv')
+    return [f"Case {row['case_id']}: {row['customer_name']}" for index, row in df_cases.iterrows()]
+
+def get_case_details(case_selection):
+    if not case_selection:
+        return "", "", gr.update(visible=False)
+
+    case_id = int(case_selection.split(':')[0].replace('Case', '').strip())
+    df_cases = pd.read_csv('customer_cases.csv')
+    case_data = df_cases[df_cases['case_id'] == case_id].iloc[0]
+
+    return case_data['customer_query'], case_data.to_json(), gr.update(visible=True)
+
+def generate_case_reply(case_data_json):
+    """Generates an email reply using the Gemini API."""
+    if not LLM_LOADED:
+        return "Gemini API not loaded. Cannot generate reply."
+
+    internal_data = json.loads(case_data_json)
+    customer_query = internal_data.pop('customer_query')
+    internal_data.pop('case_id')
+
+    system_prompt = """
+        You are an expert AI assistant for "RecoEngine", specializing in Amazon customer support...
+        (Your detailed system prompt remains exactly the same)
+        """
+    
+    user_prompt = f"### Customer Query ###\n{customer_query}\n### Internal Data ###\n{json.dumps(internal_data)}"
+    
+    # --- MODIFIED: Call the Gemini API ---
+    try:
+        response = model.generate_content(f"{system_prompt}\n{user_prompt}")
+        generated_text = response.text
+    except Exception as e:
+        return f"Error calling Gemini API: {e}"
+
+    try:
+        start_index = generated_text.find('{')
+        end_index = generated_text.rfind('}') + 1
+        json_output_str = generated_text[start_index:end_index]
+        parsed_json = json.loads(json_output_str)
+
+        template = f"""
+          Subject: Regarding your recent inquiry about your order...
+          (Your email template remains exactly the same)
+          """
+        return template
+    except Exception as e:
+        return f"Error parsing API output: {e}\nRaw Output:\n{generated_text}"
+
+def send_reply(case_selection):
+    case_id = int(case_selection.split(':')[0].replace('Case', '').strip())
+    df_cases = pd.read_csv('customer_cases.csv')
+    df_cases = df_cases[df_cases['case_id'] != case_id]
+    df_cases.to_csv('customer_cases.csv', index=False)
+    return "Reply sent, case closed.", "", "", gr.update(choices=get_open_cases(), value=None), gr.update(visible=False)
+
+# === USE CASE 3: Automated Prep Center Communication ===
+def generate_prep_center_email(client_name, shipment_id, status, arrival_date, num_cartons, labels_provided):
+    if not LLM_LOADED:
+        return "Gemini API not loaded. Cannot generate email."
+
+    shipment_data = {"client_name": client_name, "shipment_id": shipment_id, "status": status, "expected_arrival_date": arrival_date, "num_cartons": num_cartons, "labels_provided": labels_provided}
+    
+    system_prompt = """
+      You are an expert AI operations assistant...
+      (Your detailed system prompt remains exactly the same)
+      """
+    
+    user_prompt = f"Analyze this data and generate the JSON: {json.dumps(shipment_data)}"
+    
+    # --- MODIFIED: Call the Gemini API ---
+    try:
+        response = model.generate_content(f"{system_prompt}\n{user_prompt}")
+        generated_text = response.text
+    except Exception as e:
+        return f"Error calling Gemini API: {e}"
+
+    try:
+        start_index = generated_text.find('{')
+        end_index = generated_text.rfind('}') + 1
+        json_output_str = generated_text[start_index:end_index]
+        parsed_json = json.loads(json_output_str)
+
+        template = f"""
+            Subject: {parsed_json.get('urgency_tag')} - Action Required for Your Shipment...
+            (Your email template remains exactly the same)
+            """
+        return template
+    except Exception as e:
+        return f"Error parsing API output: {e}\nRaw Output:\n{generated_text}"
+
+# ... (The rest of your code for 'recommend_best_warehouse' and the Gradio UI remains exactly the same) ...
+
+# --- 4. GRADIO UI CONSTRUCTION ---
+with gr.Blocks(theme=gr.themes.Soft(text_size='lg'), title="RecoEngine Demo") as demo:
+    gr.Markdown("# RecoEngine Demo!!")
+    # ... (Your entire Gradio UI layout remains the same) ...
+
+# --- 5. LAUNCH THE APP ---
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+pandas
+prophet
+google-generative-ai
+scikit-learn