Update app.py

app.py

@@ -1,210 +1,21 @@
-import gradio as gr
-import yfinance as yf
-import pandas as pd
-import numpy as np
-from datetime import datetime, timedelta
-from googlesearch import search
-from textblob import TextBlob
-from statsmodels.tsa.holtwinters import ExponentialSmoothing
-
-# Function to fetch cryptocurrency data
-def get_crypto_data(symbol, period="30d", interval="1h"):
-    crypto = yf.Ticker(f"{symbol}-USD")
-    data = crypto.history(period=period, interval=interval)
-    return data
-
-# Function to calculate RSI
-def calculate_rsi(data, period=14):
-    delta = data['Close'].diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
-    rs = gain / loss
-    rsi = 100 - (100 / (1 + rs))
-    return rsi
-
-# Function to calculate MACD
-def calculate_macd(data, short_window=12, long_window=26, signal_window=9):
-    short_ema = data['Close'].ewm(span=short_window, adjust=False).mean()
-    long_ema = data['Close'].ewm(span=long_window, adjust=False).mean()
-    macd = short_ema - long_ema
-    signal = macd.ewm(span=signal_window, adjust=False).mean()
-    return macd, signal
-
-# Function to calculate EMA
-def calculate_ema(data, period=20):
-    return data['Close'].ewm(span=period, adjust=False).mean()
-
-# Function to calculate ATR
-def calculate_atr(data, period=14):
-    high_low = data['High'] - data['Low']
-    high_close = np.abs(data['High'] - data['Close'].shift())
-    low_close = np.abs(data['Low'] - data['Close'].shift())
-    true_range = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
-    atr = true_range.rolling(window=period).mean()
-    return atr
-
-# Function to calculate probabilities for the next 12 hours
-def calculate_probabilities(data):
-    # Calculate indicators on the entire dataset
-    data['RSI'] = calculate_rsi(data)
-    data['MACD'], data['MACD_Signal'] = calculate_macd(data)
-    data['EMA_50'] = calculate_ema(data, period=50)
-    data['EMA_200'] = calculate_ema(data, period=200)
-    data['ATR'] = calculate_atr(data)
-    
-    # Use the most recent values for predictions
-    recent_data = data.iloc[-1]
-    
-    # Calculate probabilities based on recent data
-    probabilities = {
-        "RSI": {"Pump": 0, "Dump": 0},
-        "MACD": {"Pump": 0, "Dump": 0},
-        "EMA": {"Pump": 0, "Dump": 0},
-        "ATR": {"Pump": 0, "Dump": 0},
-    }
-    
-    # RSI
-    rsi = recent_data['RSI']
-    if rsi < 25:
-        probabilities["RSI"]["Pump"] = 90  # Strong Pump
-    elif 25 <= rsi < 30:
-        probabilities["RSI"]["Pump"] = 60  # Moderate Pump
-    elif 70 < rsi <= 75:
-        probabilities["RSI"]["Dump"] = 60  # Moderate Dump
-    elif rsi > 75:
-        probabilities["RSI"]["Dump"] = 90  # Strong Dump
-    
-    # MACD
-    macd = recent_data['MACD']
-    macd_signal = recent_data['MACD_Signal']
-    if macd > macd_signal and macd > 0:
-        probabilities["MACD"]["Pump"] = 90  # Strong Pump
-    elif macd > macd_signal and macd <= 0:
-        probabilities["MACD"]["Pump"] = 60  # Moderate Pump
-    elif macd < macd_signal and macd >= 0:
-        probabilities["MACD"]["Dump"] = 60  # Moderate Dump
-    elif macd < macd_signal and macd < 0:
-        probabilities["MACD"]["Dump"] = 90  # Strong Dump
-    
-    # EMA
-    ema_short = recent_data['EMA_50']
-    ema_long = recent_data['EMA_200']
-    close = recent_data['Close']
-    if ema_short > ema_long and close > ema_short:
-        probabilities["EMA"]["Pump"] = 90  # Strong Pump
-    elif ema_short > ema_long and close <= ema_short:
-        probabilities["EMA"]["Pump"] = 60  # Moderate Pump
-    elif ema_short < ema_long and close >= ema_short:
-        probabilities["EMA"]["Dump"] = 60  # Moderate Dump
-    elif ema_short < ema_long and close < ema_short:
-        probabilities["EMA"]["Dump"] = 90  # Strong Dump
-    
-    # ATR
-    atr = recent_data['ATR']
-    if atr > 100:
-        probabilities["ATR"]["Pump"] = 90  # Strong Pump
-    elif 50 < atr <= 100:
-        probabilities["ATR"]["Pump"] = 60  # Moderate Pump
-    elif -100 <= atr < -50:
-        probabilities["ATR"]["Dump"] = 60  # Moderate Dump
-    elif atr < -100:
-        probabilities["ATR"]["Dump"] = 90  # Strong Dump
-    
-    return probabilities, recent_data
-
-# Function to predict future prices using Exponential Smoothing
-def predict_price(data, days=7):
-    try:
-        # Prepare data for Exponential Smoothing
-        df = data[['Close']]
-        
-        # Train the model
-        model = ExponentialSmoothing(df, trend="add", seasonal="add", seasonal_periods=7)
-        fit = model.fit()
-        
-        # Make future predictions
-        forecast = fit.forecast(steps=days)
-        
-        # Format predictions with dates
-        last_date = data.index[-1]
-        dates = pd.date_range(start=last_date + timedelta(days=1), periods=days)
-        forecast_df = pd.DataFrame({"Date": dates, "Price": forecast})
-        forecast_df["Date"] = forecast_df["Date"].dt.strftime("%Y-%m-%d")
-        forecast_df["Price"] = forecast_df["Price"].round(2)
-        
-        return forecast_df
-    except Exception as e:
-        return f"Error predicting prices: {e}"
-
-# Function to fetch Google search results and perform sentiment analysis
-def fetch_google_sentiment(query, num_results=5):
-    try:
-        # Fetch Google search results
-        search_results = list(search(query, stop=num_results, pause=2))
-        
-        # Perform sentiment analysis on the search results
-        sentiments = []
-        for result in search_results:
-            analysis = TextBlob(result)
-            sentiment = "Positive" if analysis.sentiment.polarity > 0 else "Negative" if analysis.sentiment.polarity < 0 else "Neutral"
-            sentiments.append({
-                "result": result,
-                "sentiment": sentiment,
-            })
-        return sentiments
-    except Exception as e:
-        return f"Error fetching Google search results: {e}"
-
-# Gradio Interface
-def crypto_app(symbol):
-    if symbol:
-        # Fetch data
-        data = get_crypto_data(symbol)
-        if data.empty:
-            return f"No data found for {symbol}. Please check the symbol and try again."
-        else:
-            # Ensure the DataFrame has enough rows
-            if len(data) < 20:
-                return f"Not enough data to calculate indicators. Only {len(data)} rows available. Please try a longer period."
-            else:
-                # Calculate probabilities for the next 12 hours
-                probabilities, recent_data = calculate_probabilities(data)
-                
-                # Predict future prices
-                price_predictions = predict_price(data)
-                
-                # Fetch Google search sentiment
-                sentiment_results = fetch_google_sentiment(f"{symbol} cryptocurrency")
-                
-                # Prepare output
-                output = f"**{symbol} Pump/Dump Probabilities (Next 12 Hours):**\n"
-                for indicator, values in probabilities.items():
-                    output += f"- **{indicator}**: Pump: {values['Pump']:.2f}%, Dump: {values['Dump']:.2f}%\n"
-                
-                output += "\n**Price Predictions (Next 7 Days):**\n"
-                if isinstance(price_predictions, pd.DataFrame):
-                    output += price_predictions.to_string(index=False)
-                else:
-                    output += price_predictions
-                
-                output += "\n\n**Google Search Sentiment Analysis:**\n"
-                if isinstance(sentiment_results, list):
-                    for result in sentiment_results:
-                        output += f"- {result['result']} ({result['sentiment']})\n"
-                else:
-                    output += sentiment_results
-                
-                return output
-    else:
-        return "Please enter a cryptocurrency symbol."
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=crypto_app,
-    inputs=gr.Textbox(placeholder="Enter cryptocurrency symbol (e.g., ETH, BTC)"),
-    outputs="text",
-    title="Crypto Information Finder and Pump/Dump Predictor 📈📉",
-    description="This app provides technical indicator-based predictions, price forecasts, and sentiment analysis for any cryptocurrency.",
-)
-
-iface.launch()
+**ETH Pump/Dump Probabilities (Next 12 Hours):**
+- RSI: Pump: 90.00%, Dump: 0.00%
+- MACD: Pump: 60.00%, Dump: 0.00%
+- EMA: Pump: 90.00%, Dump: 0.00%
+- ATR: Pump: 60.00%, Dump: 0.00%
+
+**Price Predictions (Next 7 Days):**
+Date         Price
+October 10   1800.00
+October 11   1850.00
+October 12   1900.00
+October 13   1950.00
+October 14   2000.00
+
+**Latest Crypto News Sentiment:**
+- **Ethereum Price Surges to New Highs** (Bullish) - October 10
+  [Read more](https://example.com/eth-price-surge)
+- **Ethereum Faces Resistance at $2000** (Bearish) - October 9
+  [Read more](https://example.com/eth-resistance)
+- **Ethereum Developers Announce Major Upgrade** (Neutral) - October 8
+  [Read more](https://example.com/eth-upgrade)