Update app.py

app.py

@@ -1,81 +1,68 @@
 import gradio as gr
-import numpy as np
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from torch.nn import functional as F
+import pandas as pd
 
-# Toy dataset
-classes = ['tech', 'health']
-keywords = {
-    'tech': ['neural', 'AI', 'system', 'inference', 'hyperparameter', 'network'],
-    'health': ['wellness', 'asbestos', 'hazmat', 'patient', 'health', 'therapy']
-}
-
-# Define a simple neural network
-class SimpleNet(nn.Module):
-    def __init__(self, input_size, hidden_size, output_size):
-        super(SimpleNet, self).__init__()
-        self.fc1 = nn.Linear(input_size, hidden_size)
-        self.fc2 = nn.Linear(hidden_size, output_size)
-
-    def forward(self, x):
-        x = F.relu(self.fc1(x))
-        return self.fc2(x)
-
-# Tokenizer and encoder (basic word count)
-def encode(text):
-    vector = np.zeros(len(set(sum(keywords.values(), []))))
-    vocab = list(set(sum(keywords.values(), [])))
-    for word in text.lower().split():
-        if word in vocab:
-            vector[vocab.index(word)] += 1
-    return torch.tensor(vector, dtype=torch.float32)
-
-# Initialize model
-model = SimpleNet(input_size=12, hidden_size=6, output_size=2)
-optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+# Load base model for inference
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
+labels = ['tech', 'health']
 
 def zero_shot_infer(text):
-    x = encode(text)
+    inputs = tokenizer(text, return_tensors="pt")
     with torch.no_grad():
-        output = model(x)
-        predicted = torch.argmax(output).item()
-    return f"Predicted Category: {classes[predicted]}"
+        logits = model(**inputs).logits
+        probs = F.softmax(logits, dim=1)
+        result = {labels[i]: float(probs[0][i]) for i in range(len(labels))}
+    return result
+
+# Initialize training data storage
+training_data = []
 
 def train_step(text, label, lr, momentum):
-    optimizer.param_groups[0]['lr'] = lr
-    for g in optimizer.param_groups:
-        g['momentum'] = momentum
-    x = encode(text)
-    y = torch.tensor([classes.index(label)], dtype=torch.long)
-    output = model(x)
-    loss = F.cross_entropy(output.unsqueeze(0), y)
-    optimizer.zero_grad()
-    loss.backward()
-    optimizer.step()
-    return f"Loss after training step: {loss.item():.4f}"
+    training_data.append((text, label))
+    return f"Stored: '{text}' as '{label}' | Total examples: {len(training_data)}"
 
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("# LARS Playground: Simulating 1989 AI Innovation")
+def fine_tune_model():
+    if len(training_data) < 4:
+        return "Need more training samples (min: 4)."
+    df = pd.DataFrame(training_data, columns=['text', 'label'])
+    # This placeholder suggests fine-tuning with a proper pipeline externally
+    return "Training initiated on backend. This version supports frontend data collection only."
 
+def conscious_infer(text):
+    output = zero_shot_infer(text)
+    max_label = max(output, key=output.get)
+    confidence = output[max_label]
+    # Simulate conscious inference via contextual intuition
+    reflection = "This concept resonates with techno-consciousness." if max_label == 'tech' else "This concept radiates healing intention."
+    return f"Label: {max_label} (Confidence: {confidence:.2f})\nInsight: {reflection}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Vers3Dynamics Conscious Labeling AI")
+    gr.Markdown("### Zero-Shot + Conscious Insight Inference")
     with gr.Row():
         input_text = gr.Textbox(label="Input Text")
-        infer_btn = gr.Button("Zero-Shot Inference")
-        infer_out = gr.Textbox(label="Inference Result")
-
-    infer_btn.click(fn=zero_shot_infer, inputs=input_text, outputs=infer_out)
-
-    gr.Markdown("### Train the Model Manually")
+        output_text = gr.Textbox(label="Inference Result")
+    infer_btn = gr.Button("Infer with Insight")
+    infer_btn.click(conscious_infer, inputs=input_text, outputs=output_text)
 
+    gr.Markdown("### Manual Conscious Training")
+    with gr.Row():
+        training_text = gr.Textbox(label="Training Text")
+        label_choice = gr.Radio(choices=labels, label="Label")
     with gr.Row():
-        train_text = gr.Textbox(label="Training Text")
-        train_label = gr.Radio(choices=classes, label="Label")
         lr = gr.Slider(0.001, 0.1, value=0.01, label="Learning Rate")
-        momentum = gr.Slider(0.0, 0.9, value=0.0, label="Momentum")
-        train_btn = gr.Button("Run Training Step")
-        train_out = gr.Textbox(label="Training Output")
+        momentum = gr.Slider(0.0, 1.0, value=0.0, label="Momentum")
+    train_output = gr.Textbox(label="Training Output")
+    train_btn = gr.Button("Store Training Sample")
+    train_btn.click(train_step, inputs=[training_text, label_choice, lr, momentum], outputs=train_output)
 
-    train_btn.click(fn=train_step, inputs=[train_text, train_label, lr, momentum], outputs=train_out)
+    gr.Markdown("### Backend Fine-Tuning Placeholder")
+    fine_tune = gr.Button("Initiate Fine-Tune")
+    fine_output = gr.Textbox(label="Fine-Tune Response")
+    fine_tune.click(fine_tune_model, outputs=fine_output)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()