Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from playwright.sync_api import sync_playwright
+from flax import linen as nn
+from jax import random
+import jax
+import jax.numpy as jnp
+
+# Define LLaVA model parameters
+MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
+MAX_LENGTH = 512
+NUM_BEAMS = 5
+
+# Define Flax model for action generation
+class ActionModel(nn.Module):
+    vocab_size: int
+    hidden_size: int
+    num_layers: int
+
+    def setup(self):
+        self.embedding = nn.Embed(self.vocab_size, self.hidden_size)
+        self.lstm = nn.LSTM(self.hidden_size, self.hidden_size, num_layers=self.num_layers)
+        self.dense = nn.Dense(self.vocab_size)
+
+    def __call__(self, inputs, init_state):
+        embedded = self.embedding(inputs)
+        output, new_state = self.lstm(embedded, init_state)
+        logits = self.dense(output)
+        return logits, new_state
+
+# Initialize Flax model
+vocab_size = 50257
+hidden_size = 1024
+num_layers = 2
+key = random.PRNGKey(0)
+model = ActionModel(vocab_size, hidden_size, num_layers)
+init_state = model.lstm.initialize_carry(key, (1, hidden_size))
+
+# Function to generate actions using LLaVA model
+def generate_actions(input_text, browser, page):
+    # Load LLaVA model
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+
+    # Prepare input for LLaVA
+    inputs = tokenizer(input_text, return_tensors="pt")
+    inputs = inputs.to(model.device)
+
+    # Generate response
+    outputs = model.generate(
+        input_ids=inputs.input_ids,
+        max_length=MAX_LENGTH,
+        num_beams=NUM_BEAMS,
+        temperature=0.7,
+    )
+
+    # Decode response and extract actions
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    actions = response.split("\n")
+    
+    # Perform actions
+    for action in actions:
+        if "open website" in action:
+            website = action.split(" ")[-1]
+            page.goto(website)
+        elif "click" in action:
+            selector = action.split(" ")[-1]
+            page.click(selector)
+        elif "type" in action:
+            text = action.split(" ")[-1]
+            page.type(text)
+        elif "submit" in action:
+            page.press("Enter")
+        else:
+            print(f"Action not recognized: {action}")
+
+# Function to initialize browser and page
+def initialize_browser():
+    with sync_playwright() as p:
+        browser = p.chromium.launch()
+        page = browser.new_page()
+        return browser, page
+
+# Gradio interface
+def run_agent(input_text):
+    with sync_playwright() as p:
+        browser, page = initialize_browser()
+        generate_actions(input_text, browser, page)
+        return f"Successfully executed actions based on: {input_text}"
+
+iface = gr.Interface(
+    fn=run_agent,
+    inputs=gr.Textbox(label="Enter your request"),
+    outputs=gr.Textbox(label="Response"),
+    title="Automated Agent",
+    description="Enter a task or instruction for the agent to perform."
+)
+iface.launch()