feat: add intro video

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+intro.mp4 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,216 +1,219 @@
-import os
-import uuid
-import streamlit as st
-import matplotlib.pyplot as plt
-import pandas as pd
-import requests
-
-st.set_page_config(page_title="Miragic Sales Pilot", page_icon="📊")
-
-# Display logo and title in a horizontal layout
-col1, col2 = st.columns([1, 4])
-with col1:
-    st.image("logo.png", width=100)  # Adjust width as needed
-with col2:
-    st.title("Miragic Sales Pilot")
-
-
-sales_file = None
-# st.write("Enter comma-separated numbers (e.g. `10, 20, 30`) to generate a bar chart.")
-
-def upload_sales_data(data: pd.DataFrame):
-    upload_url = os.environ['upload_url']
-    file_name = 'sample.csv'
-    files = {'file': (file_name, data.to_csv(index=False))}
-    response = requests.post(upload_url, files=files)
-    return response.json()
-
-def analyze_sales_data(sales_file: str, prompt: str):
-    analyze_url = os.environ['analyze_url']
-    
-    data = {'filename': sales_file, 'question': prompt}
-    response = requests.post(analyze_url, json=data)
-    return response.json()
-
-# Sidebar Menu
-with st.sidebar:
-    # TODO Name for product
-    # st.title("📊 Miragic Sales Pilot")
-    # st.subheader("Intelligent sales assistant")
-    st.markdown("""
-    ### 📝 Instructions
-    1. **Upload your data**: Use the file uploader below to upload your sales data
-    2. **File format**: Your file should be a CSV containing:
-       - A 'Date' column
-       - A 'Sales' column
-    3. **Sample file**: Download our sample CSV below to see the expected format
-    
-    ### 🔍 What this app does
-    - Analyzes your sales data
-    - Generates visualizations
-    - Provides intelligent insights
-    
-    ### ⚠️ Note
-    Make sure your data is clean and follows the required format to get the best results.
-    """)
-    uploaded_file = st.file_uploader("", type=["csv", "excel"])
-    if uploaded_file is not None:
-      date_found = False
-      sales_found = False
-      df = pd.read_csv(uploaded_file, parse_dates=True)
-      for column in df.columns:
-        if 'Date' in column:  
-          date_found = True
-        if 'Sales' in column:
-          sales_found = True
-      if(date_found == False or sales_found == False):
-        st.error('Please upload a csv containing both Date and Sales...')
-        st.stop()
-
-      response = upload_sales_data(df)
-      sales_file = response['csv_file']
-
-      st.success("File uploaded successfully!")
-      st.write("Your uploaded data:")
-      st.write(df)
-
-    #   df = drop(df)
-    #   df = date_format(df)
-    #   merge_sort(df)
-    #   series = group_to_three(df)
-
-      st.session_state.uploaded = True
-
-    with open(os.path.join(os.path.dirname(__file__), 'sample.csv'), 'rb') as f:
-       st.download_button("Download our sample CSV", f, file_name='sample.csv')
-
-    st.markdown("""![Visitors](https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FMiragic-AI%2FMiragic-Sales-Pilot&labelColor=%2337d67a&countColor=%23ff8a65&style=plastic&labelStyle=upper)""")
-
-
-# Initialize chat history in session state
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-if "greetings" not in st.session_state:
-    st.session_state.greetings = False
-
-# Display chat history
-for msg in st.session_state.messages:
-    with st.chat_message(msg["role"]):
-        if isinstance(msg["content"], str):
-            st.markdown(msg["content"])
-        else:
-            st.pyplot(msg["content"])
-
-# Greet user
-if not st.session_state.greetings:
-    with st.chat_message("assistant"):
-        intro = "Hey! As a sales analyst, I can analyze and forecast sales using your historical excel or csv data."
-        st.markdown(intro)
-        # Add assistant response to chat history
-        st.session_state.messages.append({"role": "assistant", "content": intro})
-        st.session_state.greetings = True
-
-# Example prompts
-example_prompts = [
-    "Top 10 best selling products",
-    "Top 10 worst selling products",
-    "Sales forecast for next month",
-    "How many items were sold in total each month?",
-    "What are the projected sales for next month?",
-    "How many unique product categories are there?",
-]
-
-example_prompts_help = [
-    "Search for the best selling products",
-    "Search for the worst selling products",
-    "Search for the projected sales for next month",
-    "Search for the number of items sold in total each month",
-    "Search for the sales forecast for next month",
-    "Search for the number of unique product categories",
-]
-
-button_cols = st.columns(3)
-button_cols_2 = st.columns(3)
-
-button_pressed = ""
-
-if button_cols[0].button(example_prompts[0], help=example_prompts_help[0]):
-    button_pressed = example_prompts[0]
-elif button_cols[1].button(example_prompts[1], help=example_prompts_help[1]):
-    button_pressed = example_prompts[1]
-elif button_cols[2].button(example_prompts[2], help=example_prompts_help[2]):
-    button_pressed = example_prompts[2]
-
-elif button_cols_2[0].button(example_prompts[3], help=example_prompts_help[3]):
-    button_pressed = example_prompts[3]
-elif button_cols_2[1].button(example_prompts[4], help=example_prompts_help[4]):
-    button_pressed = example_prompts[4]
-elif button_cols_2[2].button(example_prompts[5], help=example_prompts_help[5]):
-    button_pressed = example_prompts[5]
-
-# Chat input
-if prompt := (st.chat_input("Ask me anything about your sales data") or button_pressed):
-    # Add user message
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    with st.chat_message("user"):
-        st.markdown(prompt)
-
-    if sales_file is None:
-        st.error("Please upload a file first")
-        st.stop()
-
-    response = analyze_sales_data(sales_file, prompt)
-    print("==========>: ", response)
-
-    if response['status'] == 'success':
-        dummy_response = response['result'][0]
-
-        try:
-            # Process input
-            # values = list(map(int, prompt.strip().split(',')))
-            # labels = [f"Item {i+1}" for i in range(len(values))]
-            values = [item['y_axis'] for item in dummy_response['visualization']['data']]
-            labels = [item['x_axis'] for item in dummy_response['visualization']['data']]
-
-            # Create chart
-            fig, ax = plt.subplots()
-            if dummy_response['visualization']['type'] == 'line_chart':
-                ax.plot(labels, values, marker='o', color="lightcoral")
-            else:
-                ax.bar(labels, values, color="lightcoral")
-            plt.xticks(rotation=45, ha='right')
-            ax.set_title(dummy_response['visualization']['config']['title'])
-            ax.set_ylabel(dummy_response['visualization']['config']['y_axis'])
-            ax.set_xlabel(dummy_response['visualization']['config']['x_axis'])
-            
-            # Add value labels on top of each bar
-            for i, v in enumerate(values):
-                ax.text(i, v, f'{v:,.2f}', ha='center', va='bottom')
-
-            # Generate analysis message
-            analysis = "📊 Insights:\n"
-            for insight in dummy_response['insights']:
-                analysis += f"- {insight}\n"
-
-            # Add bot replies
-            st.session_state.messages.append({"role": "assistant", "content": fig})
-            st.session_state.messages.append({"role": "assistant", "content": analysis})
-            with st.chat_message("assistant"):
-                st.pyplot(fig)
-                st.markdown(analysis)
-
-        except Exception as e:
-            err_msg = f"❌ Error: {str(e)} — please enter only comma-separated numbers."
-            st.session_state.messages.append({"role": "assistant", "content": err_msg})
-            with st.chat_message("assistant"):
-                st.markdown(err_msg)
-    else:
-        err_msg = "❌ Error: Unable to process the request. Here are some example queries you can try:\n\n"
-        for candidate in response['candidate_questions']:
-            err_msg += f"- {candidate}\n"
-
-        st.session_state.messages.append({"role": "assistant", "content": err_msg})
-        with st.chat_message("assistant"):
-            st.markdown(err_msg)
-
-    st.rerun()
+import os
+import uuid
+import streamlit as st
+import matplotlib.pyplot as plt
+import pandas as pd
+import requests
+
+st.set_page_config(page_title="Miragic Sales Pilot", page_icon="📊")
+
+# Display logo and title in a horizontal layout
+col1, col2 = st.columns([1, 4])
+with col1:
+    st.image("logo.png", width=100)  # Adjust width as needed
+with col2:
+    st.title("Miragic Sales Pilot")
+
+
+sales_file = None
+# st.write("Enter comma-separated numbers (e.g. `10, 20, 30`) to generate a bar chart.")
+
+def upload_sales_data(data: pd.DataFrame):
+    upload_url = os.environ['upload_url']
+    file_name = 'sample.csv'
+    files = {'file': (file_name, data.to_csv(index=False))}
+    response = requests.post(upload_url, files=files)
+    return response.json()
+
+def analyze_sales_data(sales_file: str, prompt: str):
+    analyze_url = os.environ['analyze_url']
+    
+    data = {'filename': sales_file, 'question': prompt}
+    response = requests.post(analyze_url, json=data)
+    return response.json()
+
+# Sidebar Menu
+with st.sidebar:
+    # TODO Name for product
+    # st.title("📊 Miragic Sales Pilot")
+    # st.subheader("Intelligent sales assistant")
+    st.markdown("""
+    ### 📝 Instructions
+    1. **Upload your data**: Use the file uploader below to upload your sales data
+    2. **File format**: Your file should be a CSV containing:
+       - A 'Date' column
+       - A 'Sales' column
+    3. **Sample file**: Download our sample CSV below to see the expected format
+    
+    ### 🔍 What this app does
+    - Analyzes your sales data
+    - Generates visualizations
+    - Provides intelligent insights
+    
+    ### ⚠️ Note
+    Make sure your data is clean and follows the required format to get the best results.
+    """)
+    uploaded_file = st.file_uploader("", type=["csv", "excel"])
+    if uploaded_file is not None:
+      date_found = False
+      sales_found = False
+      df = pd.read_csv(uploaded_file, parse_dates=True)
+      for column in df.columns:
+        if 'Date' in column:  
+          date_found = True
+        if 'Sales' in column:
+          sales_found = True
+      if(date_found == False or sales_found == False):
+        st.error('Please upload a csv containing both Date and Sales...')
+        st.stop()
+
+      response = upload_sales_data(df)
+      sales_file = response['csv_file']
+
+      st.success("File uploaded successfully!")
+      st.write("Your uploaded data:")
+      st.write(df)
+
+    #   df = drop(df)
+    #   df = date_format(df)
+    #   merge_sort(df)
+    #   series = group_to_three(df)
+
+      st.session_state.uploaded = True
+
+    with open(os.path.join(os.path.dirname(__file__), 'sample.csv'), 'rb') as f:
+       st.download_button("Download our sample CSV", f, file_name='sample.csv')
+
+    st.markdown("""![Visitors](https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FMiragic-AI%2FMiragic-Sales-Pilot&labelColor=%2337d67a&countColor=%23ff8a65&style=plastic&labelStyle=upper)""")
+
+col1, col2, col3 = st.columns([0.01, 0.98, 0.01])
+
+col2.video("intro.mp4")
+
+# Initialize chat history in session state
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+if "greetings" not in st.session_state:
+    st.session_state.greetings = False
+
+# Display chat history
+for msg in st.session_state.messages:
+    with st.chat_message(msg["role"]):
+        if isinstance(msg["content"], str):
+            st.markdown(msg["content"])
+        else:
+            st.pyplot(msg["content"])
+
+# Greet user
+if not st.session_state.greetings:
+    with st.chat_message("assistant"):
+        intro = "Hey! As a sales analyst, I can analyze and forecast sales using your historical excel or csv data."
+        st.markdown(intro)
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": intro})
+        st.session_state.greetings = True
+
+# Example prompts
+example_prompts = [
+    "Top 10 best selling products",
+    "Top 10 worst selling products",
+    "Sales forecast for next month",
+    "How many items were sold in total each month?",
+    "What are the projected sales for next month?",
+    "How many unique product categories are there?",
+]
+
+example_prompts_help = [
+    "Search for the best selling products",
+    "Search for the worst selling products",
+    "Search for the projected sales for next month",
+    "Search for the number of items sold in total each month",
+    "Search for the sales forecast for next month",
+    "Search for the number of unique product categories",
+]
+
+button_cols = st.columns(3)
+button_cols_2 = st.columns(3)
+
+button_pressed = ""
+
+if button_cols[0].button(example_prompts[0], help=example_prompts_help[0]):
+    button_pressed = example_prompts[0]
+elif button_cols[1].button(example_prompts[1], help=example_prompts_help[1]):
+    button_pressed = example_prompts[1]
+elif button_cols[2].button(example_prompts[2], help=example_prompts_help[2]):
+    button_pressed = example_prompts[2]
+
+elif button_cols_2[0].button(example_prompts[3], help=example_prompts_help[3]):
+    button_pressed = example_prompts[3]
+elif button_cols_2[1].button(example_prompts[4], help=example_prompts_help[4]):
+    button_pressed = example_prompts[4]
+elif button_cols_2[2].button(example_prompts[5], help=example_prompts_help[5]):
+    button_pressed = example_prompts[5]
+
+# Chat input
+if prompt := (st.chat_input("Ask me anything about your sales data") or button_pressed):
+    # Add user message
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    if sales_file is None:
+        st.error("Please upload a file first")
+        st.stop()
+
+    response = analyze_sales_data(sales_file, prompt)
+    print("==========>: ", response)
+
+    if response['status'] == 'success':
+        dummy_response = response['result'][0]
+
+        try:
+            # Process input
+            # values = list(map(int, prompt.strip().split(',')))
+            # labels = [f"Item {i+1}" for i in range(len(values))]
+            values = [item['y_axis'] for item in dummy_response['visualization']['data']]
+            labels = [item['x_axis'] for item in dummy_response['visualization']['data']]
+
+            # Create chart
+            fig, ax = plt.subplots()
+            if dummy_response['visualization']['type'] == 'line_chart':
+                ax.plot(labels, values, marker='o', color="lightcoral")
+            else:
+                ax.bar(labels, values, color="lightcoral")
+            plt.xticks(rotation=45, ha='right')
+            ax.set_title(dummy_response['visualization']['config']['title'])
+            ax.set_ylabel(dummy_response['visualization']['config']['y_axis'])
+            ax.set_xlabel(dummy_response['visualization']['config']['x_axis'])
+            
+            # Add value labels on top of each bar
+            for i, v in enumerate(values):
+                ax.text(i, v, f'{v:,.2f}', ha='center', va='bottom')
+
+            # Generate analysis message
+            analysis = "📊 Insights:\n"
+            for insight in dummy_response['insights']:
+                analysis += f"- {insight}\n"
+
+            # Add bot replies
+            st.session_state.messages.append({"role": "assistant", "content": fig})
+            st.session_state.messages.append({"role": "assistant", "content": analysis})
+            with st.chat_message("assistant"):
+                st.pyplot(fig)
+                st.markdown(analysis)
+
+        except Exception as e:
+            err_msg = f"❌ Error: {str(e)} — please enter only comma-separated numbers."
+            st.session_state.messages.append({"role": "assistant", "content": err_msg})
+            with st.chat_message("assistant"):
+                st.markdown(err_msg)
+    else:
+        err_msg = "❌ Error: Unable to process the request. Here are some example queries you can try:\n\n"
+        for candidate in response['candidate_questions']:
+            err_msg += f"- {candidate}\n"
+
+        st.session_state.messages.append({"role": "assistant", "content": err_msg})
+        with st.chat_message("assistant"):
+            st.markdown(err_msg)
+
+    st.rerun()

intro.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b0ecfb205220f88f8b21c3c3d4fb4f7e6578153178088a07040b4c9a4aa7c07c
+size 27648329