Update app.py

app.py

@@ -1,21 +1,68 @@
 import gradio as gr
 from transformers import pipeline
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score
 
-# Load the sentiment analysis model from Hugging Face
+# Step 1: Sentiment Analysis Pipeline
 sentiment_analyzer = pipeline("sentiment-analysis")
 
-# Define a function that will analyze sentiment
 def analyze_sentiment(text):
     result = sentiment_analyzer(text)
-    return result[0]['label'], result[0]['score']
-
-# Create a Gradio interface for the sentiment analysis function
-iface = gr.Interface(
-    fn=analyze_sentiment,
-    inputs=gr.Textbox(label="Enter text for sentiment analysis"),
-    outputs=[gr.Textbox(label="Sentiment Label"), gr.Textbox(label="Confidence Score")],
-    live=True
+    return result[0]  # Return the result (positive/negative)
+
+# Step 2: Machine Learning Model Suggestion and Classification
+def suggest_ml_models(dataset):
+    # You can add logic to select appropriate models based on the dataset
+    models = {
+        "Random Forest": RandomForestClassifier(),
+        "Logistic Regression": LogisticRegression(),
+        "Support Vector Classifier": SVC(),
+    }
+    
+    # Assuming dataset is in the form (X, y)
+    X_train, X_test, y_train, y_test = train_test_split(dataset[0], dataset[1], test_size=0.3, random_state=42)
+
+    model_accuracies = {}
+    for model_name, model in models.items():
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_test)
+        accuracy = accuracy_score(y_test, y_pred)
+        model_accuracies[model_name] = accuracy
+
+    return model_accuracies
+
+# Step 3: Create a synthetic dataset for classification
+def create_synthetic_dataset():
+    # Creating a synthetic classification dataset
+    X, y = make_classification(n_samples=1000, n_features=20, random_state=42)
+    return X, y
+
+# Combine both functions in Gradio for user interaction
+def analyze_and_suggest(text):
+    # Sentiment analysis
+    sentiment_result = analyze_sentiment(text)
+    
+    # Generate synthetic dataset
+    dataset = create_synthetic_dataset()
+    
+    # Model suggestion for classification
+    model_accuracies = suggest_ml_models(dataset)
+
+    # Return both the sentiment analysis result and model suggestions
+    return sentiment_result, model_accuracies
+
+# Gradio interface for user interaction
+interface = gr.Interface(
+    fn=analyze_and_suggest,
+    inputs=gr.Textbox(label="Enter Text for Sentiment Analysis"),
+    outputs=[gr.JSON(label="Sentiment Analysis Result"), gr.JSON(label="Model Suggestions and Accuracies")],
+    title="Sentiment Analysis and ML Model Suggestion"
 )
 
-# Launch the interface
-iface.launch()
+# Launch Gradio Interface
+interface.launch()
+