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989ca35 3f736da 989ca35 3f736da 989ca35 3f736da 989ca35 3f736da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | 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
# Step 1: Sentiment Analysis Pipeline
sentiment_analyzer = pipeline("sentiment-analysis")
def analyze_sentiment(text):
result = sentiment_analyzer(text)
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 Gradio Interface
interface.launch()
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