wew

app.py

@@ -1,56 +1,49 @@
 import gradio as gr
-import requests
-import pandas as pd
+from transformers import pipeline
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, roc_curve, auc
 import matplotlib.pyplot as plt
-from datetime import datetime
-
-API_KEY = "PJRAUD6KHJ2O097X"
-
-def get_stock_data(symbol, start_date, end_date):
-    url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&apikey={API_KEY}"
-    response = requests.get(url)
-    data = response.json()
-
-    if "Time Series (Daily)" in data:
-        df = pd.DataFrame(data["Time Series (Daily)"]).T
-        df.index = pd.to_datetime(df.index)
-        
-        # Convert input dates to datetime
-        try:
-            start_datetime = datetime.strptime(start_date, '%Y-%m-%d')
-            end_datetime = datetime.strptime(end_date, '%Y-%m-%d')
-            df = df.loc[start_datetime:end_datetime]
-        except ValueError:
-            return "Invalid date format. Please use YYYY-MM-DD.", None
-
-        df.columns = ["Open", "High", "Low", "Close", "Volume"]
-        
-        # Plotting
-        plt.figure(figsize=(10, 5))
-        plt.plot(df['Close'], label='Close Price')
-        plt.title(f'Closing Price of {symbol}')
-        plt.xlabel('Date')
-        plt.ylabel('Price')
-        plt.legend()
-        plt.grid(True)
-        plt.xticks(rotation=45)
-        plt.tight_layout()
-
-        return df.to_html(), gr.Image.from_matplotlib(plt)
-    else:
-        return "Data not found or invalid stock symbol.", None
-
-iface = gr.Interface(
-    fn=get_stock_data,
-    inputs=[
-        gr.Textbox(label="Stock Symbol", placeholder="Enter a stock symbol (like AAPL, MSFT)"),
-        gr.Textbox(label="Start Date", placeholder="YYYY-MM-DD"),
-        gr.Textbox(label="End Date", placeholder="YYYY-MM-DD")
-    ],
-    outputs=["html", "plot"],
-    title="Personalized Stock Market Data App",
-    description="Enter a stock symbol and date range to fetch its daily time series data."
+import numpy as np
+
+# Initialize the sentiment analysis pipeline with a multilingual model
+sentiment_analysis = pipeline("sentiment-analysis", model="bert-base-multilingual-cased")
+
+def analyze_sentiment(text):
+    result = sentiment_analysis(text)
+    return result[0]
+
+# Mock functions to calculate metrics - Replace with actual implementation
+def calculate_metrics(y_true, y_pred):
+    accuracy = accuracy_score(y_true, y_pred)
+    precision, recall, f1, _ = precision_recall_fscore_support(y_true, y_pred, average='binary')
+    cm = confusion_matrix(y_true, y_pred)
+    fpr, tpr, _ = roc_curve(y_true, y_pred)
+    roc_auc = auc(fpr, tpr)
+    return accuracy, precision, recall, f1, cm, fpr, tpr, roc_auc
+
+def plot_confusion_matrix(cm):
+    # Plot confusion matrix here
+    pass
+
+def plot_roc_curve(fpr, tpr, roc_auc):
+    # Plot ROC curve here
+    pass
+
+# Replace this with actual test data and predictions
+y_true = [0, 1, 0, 1]  # True labels
+y_pred = [0, 1, 0, 1]  # Predicted labels
+
+# Calculate metrics
+accuracy, precision, recall, f1, cm, fpr, tpr, roc_auc = calculate_metrics(y_true, y_pred)
+
+# Plot confusion matrix and ROC curve
+plot_confusion_matrix(cm)
+plot_roc_curve(fpr, tpr, roc_auc)
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter Text Here..."),
+    outputs="text"
 )
 
-if __name__ == "__main__":
-    iface.launch()
+interface.launch()

requirements.txt

@@ -1 +1,4 @@
-gradio
+transformers==4.36.2
+gradio==4.14.0
+scikit-learn
+matplotlib