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app. py (1).txt

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import io
+
+def trading_dashboard(digit, bias, digit_bias_list, bias_file, digit_sequence):
+    # Bias interpretation + gauge zone
+    if bias < 30:
+        bias_msg = f"Bias {bias}% → Weak (Red Zone)"
+        gauge_color = "red"
+    elif 30 <= bias < 60:
+        bias_msg = f"Bias {bias}% → Moderate (Yellow Zone)"
+        gauge_color = "yellow"
+    else:
+        bias_msg = f"Bias {bias}% → Strong (Green Zone)"
+        gauge_color = "green"
+
+    # --- Bias Gauge Plot ---
+    fig_gauge, axg = plt.subplots(figsize=(4,1))
+    axg.barh(["Bias"], [bias], color=gauge_color)
+    axg.set_xlim(0,100)
+    axg.set_title("Bias Strength Gauge")
+    axg.set_xlabel("%")
+    for spine in axg.spines.values():
+        spine.set_visible(False)
+
+    # --- Digit Bias Analyzer (0–9 bar chart) ---
+    if bias_file is not None:
+        try:
+            df = pd.read_csv(bias_file) if bias_file.name.endswith(".csv") else pd.read_excel(bias_file)
+            digit_bias_values = df.iloc[0].values.tolist()[:10]
+        except Exception:
+            digit_bias_values = np.random.randint(0,100,10)
+    elif digit_bias_list is not None and len(digit_bias_list) == 10:
+        digit_bias_values = digit_bias_list
+    else:
+        digit_bias_values = np.random.randint(0,100,10)
+
+    fig_digits, axd = plt.subplots()
+    axd.bar(range(10), digit_bias_values, color="skyblue")
+    axd.set_xticks(range(10))
+    axd.set_xticklabels([str(i) for i in range(10)])
+    axd.set_title("Digit Bias Analyzer (0–9)")
+    axd.set_ylabel("Bias %")
+
+    # --- Digit Psychology Module ---
+    streaks = []
+    bias_summary = {}
+    if digit_sequence:
+        digits = [int(d) for d in str(digit_sequence) if d.isdigit()]
+        if digits:
+            # Streak awareness
+            current_streak = 1
+            for i in range(1, len(digits)):
+                if digits[i] == digits[i-1]:
+                    current_streak += 1
+                else:
+                    streaks.append((digits[i-1], current_streak))
+                    current_streak = 1
+            streaks.append((digits[-1], current_streak))
+
+            # Bias summary (frequency %)
+            freq = pd.Series(digits).value_counts(normalize=True) * 100
+            bias_summary = freq.to_dict()
+
+    psychology_text = "📊 Digit Psychology Analysis\n\n"
+    if streaks:
+        psychology_text += "Streaks:\n" + "\n".join([f"Digit {d} → {s} times" for d,s in streaks]) + "\n\n"
+    if bias_summary:
+        psychology_text += "Bias Summary (%):\n" + "\n".join([f"Digit {d}: {round(p,1)}%" for d,p in bias_summary.items()]) + "\n\n"
+    psychology_text += "Checklist:\n- Bias > 30%\n- Streak awareness checked\n- Indicators aligned (MACD/RSI)\n- Candle confirmation done"
+
+    # Example market data
+    data = pd.DataFrame(np.random.randn(50, 3), columns=['Price', 'Volume', 'Signal'])
+    data['MACD'] = data['Price'].ewm(span=12).mean() - data['Price'].ewm(span=26).mean()
+    data['RSI'] = 100 - (100 / (1 + (data['Price'].diff().clip(lower=0).rolling(14).mean() /
+                                    data['Price'].diff().clip(upper=0).abs().rolling(14).mean())))
+
+    # Plot MACD
+    fig1, ax1 = plt.subplots()
+    ax1.plot(data['Price'], label="Price")
+    ax1.plot(data['MACD'], label="MACD", color="orange")
+    ax1.set_title("Price vs MACD")
+    ax1.legend()
+
+    # Plot RSI
+    fig2, ax2 = plt.subplots()
+    ax2.plot(data['RSI'], label="RSI", color="green")
+    ax2.axhline(70, linestyle="--", color="red")
+    ax2.axhline(30, linestyle="--", color="blue")
+    ax2.set_title("RSI Indicator")
+    ax2.legend()
+
+    # Checklist summary
+    checklist = [
+        "Bias > 30%",
+        "Even/Odd bias matches entry",
+        "Over/Under bias matches entry",
+        "Indicators aligned (MACD/RSI)",
+        "Final candle confirmation"
+    ]
+
+    # Master Trading Board Poster text
+    poster = """
+📌 Master Trading Board Poster
+
+Bias Rules:
+- Trade only if Bias > 30%
+- Strong bias preferred (>60%)
+
+Even/Odd Rules:
+- Trade EVEN if bias favors even digits
+- Trade ODD if bias favors odd digits
+
+Over/Under Rules:
+- Trade OVER if bias favors digits 5–9
+- Trade UNDER if bias favors digits 0–4
+
+Indicator Rules:
+- MACD confirms trend
+- RSI not overbought/oversold
+- Candle pattern matches strategy
+"""
+
+    # --- Export results to CSV ---
+    export_df = pd.DataFrame({
+        "Digit Bias Values": digit_bias_values,
+        "Checklist": checklist
+    })
+    buffer = io.StringIO()
+    export_df.to_csv(buffer, index=False)
+    buffer.seek(0)
+
+    return bias_msg, fig_gauge, fig_digits, fig1, fig2, checklist, poster, psychology_text, buffer
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            digit = gr.Number(label="Digit (0–9)")
+            bias = gr.Slider(0, 100, step=5, label="Bias %")
+            digit_bias_list = gr.Dataframe(headers=[str(i) for i in range(10)], row_count=1, col_count=10,
+                                           label="Digit Bias Input (0–9)", type="numpy")
+            bias_file = gr.File(label="Upload Bias Data (CSV/Excel)", file_types=[".csv", ".xlsx"])
+            digit_sequence = gr.Textbox(label="Digit Sequence (e.g. 1234555777)")
+            bias_out = gr.Textbox(label="Bias Interpretation")
+            gauge_plot = gr.Plot(label="Bias Gauge")
+            digit_bias_plot = gr.Plot(label="Digit Bias Analyzer")
+            macd_plot = gr.Plot(label="MACD Chart")
+            rsi_plot = gr.Plot(label="RSI Chart")
+            psychology_out = gr.Textbox(label="Digit Psychology")
+            download_out = gr.File(label="Download Results (CSV)")
+        with gr.Column():
+            checklist_out = gr.Label(label="Checklist")
+            poster_out = gr.Textbox(label="Master Trading Board Poster")
+
+    demo.load(trading_dashboard,
+              inputs=[digit, bias, digit_bias_list, bias_file, digit_sequence],
+              outputs=[bias_out, gauge_plot, digit_bias_plot, macd_plot, rsi_plot, checklist_out, poster_out, psychology_out, download_out])
+
+demo.launch()

requirement .txt

@@ -0,0 +1,409 @@
+app.py
+
+`python
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+Page setup
+st.setpageconfig(
+    page_title="Morgan Trading Hub",
+    pageicon="assets/morganicon.png",
+    layout="wide"
+)
+
+Title and intro
+st.title("📊 Morgan Trading Hub")
+st.write("Digit Bias Analyzer | Psychology Analyzer | Performance Tracker")
+
+--- Section 1: Digit Bias Analyzer ---
+st.header("Digit Bias Analyzer")
+digits = list(range(10))
+frequency = np.random.randint(5, 20, size=10)  # placeholder demo data
+df = pd.DataFrame({"Digit": digits, "Frequency": frequency})
+fig = px.bar(df, x="Digit", y="Frequency", title="Digit Bias Frequency")
+st.plotlychart(fig, usecontainer_width=True)
+
+--- Section 2: Psychology Analyzer ---
+st.header("Psychology Analyzer")
+st.write("This section will show MACD, RSI, and candle psychology insights.")
+
+Example placeholder chart
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+plt.plot(x, y)
+st.pyplot(plt)
+
+--- Section 3: Performance Tracker ---
+st.header("Performance Tracker")
+st.write("Track trading performance and discipline checklist here.")
+performance_data = pd.DataFrame({
+    "Trade": ["Over 2", "Over 3", "Under 7", "Under 8"],
+    "Result": ["Win", "Loss", "Win", "Loss"]
+})
+st.table(performance_data)
+
+Footer
+st.markdown("---")
+st.markdown("Morgan Prince | 📞 0793 487 816 | 🇰🇪")
+`app.py
+
+`python
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+Page setup
+st.setpageconfig(
+    page_title="Morgan Trading Hub",
+    pageicon="assets/morganicon.png",
+    layout="wide"
+)
+
+Title and intro
+st.title("📊 Morgan Trading Hub")
+st.write("Digit Bias Analyzer | Psychology Analyzer | Performance Tracker")
+
+--- Section 1: Digit Bias Analyzer ---
+st.header("Digit Bias Analyzer")
+digits = list(range(10))
+frequency = np.random.randint(5, 20, size=10)  # placeholder demo data
+df = pd.DataFrame({"Digit": digits, "Frequency": frequency})
+fig = px.bar(df, x="Digit", y="Frequency", title="Digit Bias Frequency")
+st.plotlychart(fig, usecontainer_width=True)
+
+--- Section 2: Psychology Analyzer ---
+st.header("Psychology Analyzer")
+st.write("This section will show MACD, RSI, and candle psychology insights.")
+
+Example placeholder chart
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+plt.plot(x, y)
+st.pyplot(plt)
+
+--- Section 3: Performance Tracker ---
+st.header("Performance Tracker")
+st.write("Track trading performance and discipline checklist here.")
+performance_data = pd.DataFrame({
+    "Trade": ["Over 2", "Over 3", "Under 7", "Under 8"],
+    "Result": ["Win", "Loss", "Win", "Loss"]
+})
+st.table(performance_data)
+
+Footer
+st.markdown("---")
+st.markdown("Morgan Prince | 📞 0793 487 816 | 🇰🇪")
+`app.py
+
+`python
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+Page setup
+st.setpageconfig(
+    page_title="Morgan Trading Hub",
+    pageicon="assets/morganicon.png",
+    layout="wide"
+)
+
+Title and intro
+st.title("📊 Morgan Trading Hub")
+st.write("Digit Bias Analyzer | Psychology Analyzer | Performance Tracker")
+
+--- Section 1: Digit Bias Analyzer ---
+st.header("Digit Bias Analyzer")
+digits = list(range(10))
+frequency = np.random.randint(5, 20, size=10)  # placeholder demo data
+df = pd.DataFrame({"Digit": digits, "Frequency": frequency})
+fig = px.bar(df, x="Digit", y="Frequency", title="Digit Bias Frequency")
+st.plotlychart(fig, usecontainer_width=True)
+
+--- Section 2: Psychology Analyzer ---
+st.header("Psychology Analyzer")
+st.write("This section will show MACD, RSI, and candle psychology insights.")
+
+Example placeholder chart
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+plt.plot(x, y)
+st.pyplot(plt)
+
+--- Section 3: Performance Tracker ---
+st.header("Performance Tracker")
+st.write("Track trading performance and discipline checklist here.")
+performance_data = pd.DataFrame({
+    "Trade": ["Over 2", "Over 3", "Under 7", "Under 8"],
+    "Result": ["Win", "Loss", "Win", "Loss"]
+})
+st.table(performance_data)
+
+Footer
+st.markdown("---")
+st.markdown("Morgan Prince | 📞 0793 487 816 | 🇰🇪")
+`app.py
+
+`python
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+Page setup
+st.setpageconfig(
+    page_title="Morgan Trading Hub",
+    pageicon="assets/morganicon.png",
+    layout="wide"
+)
+
+Title and intro
+st.title("📊 Morgan Trading Hub")
+st.write("Digit Bias Analyzer | Psychology Analyzer | Performance Tracker")
+
+--- Section 1: Digit Bias Analyzer ---
+st.header("Digit Bias Analyzer")
+digits = list(range(10))
+frequency = np.random.randint(5, 20, size=10)  # placeholder demo data
+df = pd.DataFrame({"Digit": digits, "Frequency": frequency})
+fig = px.bar(df, x="Digit", y="Frequency", title="Digit Bias Frequency")
+st.plotlychart(fig, usecontainer_width=True)
+
+--- Section 2: Psychology Analyzer ---
+st.header("Psychology Analyzer")
+st.write("This section will show MACD, RSI, and candle psychology insights.")
+
+Example placeholder chart
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+plt.plot(x, y)
+st.pyplot(plt)
+
+--- Section 3: Performance Tracker ---
+st.header("Performance Tracker")
+st.write("Track trading performance and discipline checklist here.")
+performance_data = pd.DataFrame({
+    "Trade": ["Over 2", "Over 3", "Under 7", "Under 8"],
+    "Result": ["Win", "Loss", "Win", "Loss"]
+})
+st.table(performance_data)
+
+Footer
+st.markdown("---")
+st.markdown("Morgan Prince | 📞 0793 487 816 | 🇰🇪")
+`app.py
+
+`python
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+Page setup
+st.setpageconfig(
+    page_title="Morgan Trading Hub",
+    pageicon="assets/morganicon.png",
+    layout="wide"
+)
+
+Title and intro
+st.title("📊 Morgan Trading Hub")
+st.write("Digit Bias Analyzer | Psychology Analyzer | Performance Tracker")
+
+--- Section 1: Digit Bias Analyzer ---
+st.header("Digit Bias Analyzer")
+digits = list(range(10))
+frequency = np.random.randint(5, 20, size=10)  # placeholder demo data
+df = pd.DataFrame({"Digit": digits, "Frequency": frequency})
+fig = px.bar(df, x="Digit", y="Frequency", title="Digit Bias Frequency")
+st.plotlychart(fig, usecontainer_width=True)
+
+--- Section 2: Psychology Analyzer ---
+st.header("Psychology Analyzer")
+st.write("This section will show MACD, RSI, and candle psychology insights.")
+
+Example placeholder chart
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+plt.plot(x, y)
+st.pyplot(plt)
+
+--- Section 3: Performance Tracker ---
+st.header("Performance Tracker")
+st.write("Track trading performance and discipline checklist here.")
+performance_data = pd.DataFrame({
+    "Trade": ["Over 2", "Over 3", "Under 7", "Under 8"],
+    "Result": ["Win", "Loss", "Win", "Loss"]
+})
+st.table(performance_data)
+
+Footer
+st.markdown("---")
+st.markdown("Morgan Prince | 📞 0793 487 816 | 🇰🇪")
+`app.py
+
+`python
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+Page setup
+st.setpageconfig(
+    page_title="Morgan Trading Hub",
+    pageicon="assets/morganicon.png",
+    layout="wide"
+)
+
+Title and intro
+st.title("📊 Morgan Trading Hub")
+st.write("Digit Bias Analyzer | Psychology Analyzer | Performance Tracker")
+
+--- Section 1: Digit Bias Analyzer ---
+st.header("Digit Bias Analyzer")
+digits = list(range(10))
+frequency = np.random.randint(5, 20, size=10)  # placeholder demo data
+df = pd.DataFrame({"Digit": digits, "Frequency": frequency})
+fig = px.bar(df, x="Digit", y="Frequency", title="Digit Bias Frequency")
+st.plotlychart(fig, usecontainer_width=True)
+
+--- Section 2: Psychology Analyzer ---
+st.header("Psychology Analyzer")
+st.write("This section will show MACD, RSI, and candle psychology insights.")
+
+Example placeholder chart
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+plt.plot(x, y)
+st.pyplot(plt)
+
+--- Section 3: Performance Tracker ---
+st.header("Performance Tracker")
+st.write("Track trading performance and discipline checklist here.")
+performance_data = pd.DataFrame({
+    "Trade": ["Over 2", "Over 3", "Under 7", "Under 8"],
+    "Result": ["Win", "Loss", "Win", "Loss"]
+})
+st.table(performance_data)
+
+Footer
+st.markdown("---")
+st.markdown("Morgan Prince | 📞 0793 487 816 | 🇰🇪")
+`app.py
+
+`python
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+Page setup
+st.setpageconfig(
+    page_title="Morgan Trading Hub",
+    pageicon="assets/morganicon.png",
+    layout="wide"
+)
+
+Title and intro
+st.title("📊 Morgan Trading Hub")
+st.write("Digit Bias Analyzer | Psychology Analyzer | Performance Tracker")
+
+--- Section 1: Digit Bias Analyzer ---
+st.header("Digit Bias Analyzer")
+digits = list(range(10))
+frequency = np.random.randint(5, 20, size=10)  # placeholder demo data
+df = pd.DataFrame({"Digit": digits, "Frequency": frequency})
+fig = px.bar(df, x="Digit", y="Frequency", title="Digit Bias Frequency")
+st.plotlychart(fig, usecontainer_width=True)
+
+--- Section 2: Psychology Analyzer ---
+st.header("Psychology Analyzer")
+st.write("This section will show MACD, RSI, and candle psychology insights.")
+
+Example placeholder chart
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+plt.plot(x, y)
+st.pyplot(plt)
+
+--- Section 3: Performance Tracker ---
+st.header("Performance Tracker")
+st.write("Track trading performance and discipline checklist here.")
+performance_data = pd.DataFrame({
+    "Trade": ["Over 2", "Over 3", "Under 7", "Under 8"],
+    "Result": ["Win", "Loss", "Win", "Loss"]
+})
+st.table(performance_data)
+
+Footer
+st.markdown("---")
+st.markdown("Morgan Prince | 📞 0793 487 816 | 🇰🇪")
+`app.py
+
+`python
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+Page setup
+st.setpageconfig(
+    page_title="Morgan Trading Hub",
+    pageicon="assets/morganicon.png",
+    layout="wide"
+)
+
+Title and intro
+st.title("📊 Morgan Trading Hub")
+st.write("Digit Bias Analyzer | Psychology Analyzer | Performance Tracker")
+
+--- Section 1: Digit Bias Analyzer ---
+st.header("Digit Bias Analyzer")
+digits = list(range(10))
+frequency = np.random.randint(5, 20, size=10)  # placeholder demo data
+df = pd.DataFrame({"Digit": digits, "Frequency": frequency})
+fig = px.bar(df, x="Digit", y="Frequency", title="Digit Bias Frequency")
+st.plotlychart(fig, usecontainer_width=True)
+
+--- Section 2: Psychology Analyzer ---
+st.header("Psychology Analyzer")
+st.write("This section will show MACD, RSI, and candle psychology insights.")
+
+Example placeholder chart
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+plt.plot(x, y)
+st.pyplot(plt)
+
+--- Section 3: Performance Tracker ---
+st.header("Performance Tracker")
+st.write("Track trading performance and discipline checklist here.")
+performance_data = pd.DataFrame({
+    "Trade": ["Over 2", "Over 3", "Under 7", "Under 8"],
+    "Result": ["Win", "Loss", "Win", "Loss"]
+})
+st.table(performance_data)
+
+Footer
+st.markdown("---")
+st.markdown("Morgan Prince | 📞 0793 487 816 | 🇰🇪")
+`