Version 2.1

app.py

@@ -1,151 +1,46 @@
 import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+import pickle
+from transformers import pipeline
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
+def load_model(selected_model):
+    with open(selected_model, 'rb') as file:
+        loaded_model = pickle.load(file)
+    return loaded_model
+
+def predict(model, text):
+    encoder = {
+        0:'assets/negative.jpeg',
+        1:'assets/neutral.jpeg',
+        2:'assets/positive.jpeg'
+    }
+    selected_model = None
+    with open('vectorizer.pkl', 'rb') as file:
+        vectorizer = pickle.load(file)
+
+    if 'Random Forest' == model:
+        selected_model = "models/rf_twitter.pkl"
+    elif 'Logistic Regression' == model:
+        selected_model = "models/lg_twitter.pkl"
+    elif 'Naive Bayes' == model:
+        selected_model = "models/nb_twitter.pkl"
+    elif 'Decision Tree' == model:
+        selected_model = "models/dt_twitter.pkl"
+    elif 'KNN' == model:
+        selected_model = "models/knn_twitter.pkl"
+    else:
+        selected_model = "models/lg_twitter.pkl"
+    loaded_model = load_model(selected_model)
+    text_vector = vectorizer.transform([text])
+    prediction = loaded_model.predict(text_vector)
+    return encoder[prediction[0]]
     
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image
-
-from transformers import pipeline
 classifier = pipeline(task="zero-shot-classification", model="facebook/bart-large-mnli")
-
 def analyze_sentiment(text):
   results = classifier(text, ["positive", "negative", "neutral"], multi_label=True)
   sentiment = max(results['labels'], key=results['scores'].__getitem__)
   return sentiment
 
-examples = [
-  "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-  "An astronaut riding a green horse",
-  "A delicious ceviche cheesecake slice",
-]
-
-css="""
-#col-container {
-  margin: 0 auto;
-  max-width: 520px;
-}
-"""
-
-if torch.cuda.is_available():
-  power_device = "GPU"
-else:
-  power_device = "CPU"
-
-with gr.Blocks(css=css) as demo:
-  
-  with gr.Column(elem_id="col-container"):
-    gr.Markdown(f"""
-      # Text-to-Image Gradio Template
-      Currently running on {power_device}.
-    """)
-    
-    with gr.Row():
-      prompt = gr.Text(
-        label="Prompt",
-        show_label=False,
-        max_lines=1,
-        placeholder="Enter your prompt",
-        container=False,
-      )
-      
-      run_button = gr.Button("Run", scale=0)
-    
-    sentiment_text = gr.Text(label="Sentiment:", show_label=True, value="")
-    result = gr.Image(label="Result", show_label=False)
-
-    with gr.Accordion("Advanced Settings", open=False):
-      negative_prompt = gr.Text(
-        label="Negative prompt",
-        max_lines=1,
-        placeholder="Enter a negative prompt",
-        visible=False,
-      )
-      
-      seed = gr.Slider(
-        label="Seed",
-        minimum=0,
-        maximum=MAX_SEED,
-        step=1,
-        value=0,
-      )
-      
-      randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-      
-      with gr.Row():
-        width = gr.Slider(
-          label="Width",
-          minimum=256,
-          maximum=MAX_IMAGE_SIZE,
-          step=32,
-          value=512,
-        )
-        
-        height = gr.Slider(
-          label="Height",
-          minimum=256,
-          maximum=MAX_IMAGE_SIZE,
-          step=32,
-          value=512,
-        )
-      
-      with gr.Row():
-        guidance_scale = gr.Slider(
-          label="Guidance scale",
-          minimum=0.0,
-          maximum=10.0,
-          step=0.1,
-          value=0.0,
-        )
-        
-        num_inference_steps = gr.Slider(
-          label="Number of inference steps",
-          minimum=1,
-          maximum=12,
-          step=1,
-          value=2,
-        )
-    
-    gr.Examples(
-      examples = examples,
-      inputs = [prompt]
-    )
-
-  run_button.click(
-    fn = infer,
-    inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-    outputs = [result, sentiment_text]  # Update outputs to include sentiment text
-  )
-
-demo.queue().launch()
+# models = gr.Radio(['Random Forest', 'Logistic Regression','Naive Bayes','Decision Tree','KNN'], label="Choose model")
+# demo = gr.Interface(fn=predict, inputs=[models,"text"], outputs="image", title="Sentiment Analysis") 
+demo = gr.Interface(fn=analyze_sentiment, inputs="text", outputs="text", title="Sentiment Analysis") 
+demo.launch()