feat: refactoring of the code

examples/openai/screenshot_scraper.py

@@ -29,8 +29,8 @@ graph_config = {
 # ************************************************
 
 smart_scraper_graph = ScreenshotScraperGraph(
-    prompt="List me the email of the company",
+    prompt="List me all the projects",
-    source="https://scrapegraphai.com/",
+    source="https://perinim.github.io/projects/",
     config=graph_config
 )
 

scrapegraphai/nodes/fetch_screen_node.py

@@ -1,3 +1,6 @@
+"""
+fetch_screen_node module
+"""
 from typing import List, Optional
 from playwright.sync_api import sync_playwright
 from .base_node import BaseNode
@@ -18,7 +21,9 @@ class FetchScreenNode(BaseNode):
         self.url = node_config.get("link")
 
     def execute(self, state: dict) -> dict:
-        """Captures screenshots from the input URL and stores them in the state dictionary as bytes."""
+        """
+        Captures screenshots from the input URL and stores them in the state dictionary as bytes.
+        """
 
         screenshots = []
 
@@ -29,28 +34,25 @@ class FetchScreenNode(BaseNode):
 
             viewport_height = page.viewport_size["height"]
 
-            # Initialize screenshot counter
             screenshot_counter = 1
 
-            # List to keep track of screenshot data
             screenshot_data_list = []
 
-            # Function to capture screenshots
             def capture_screenshot(scroll_position, counter):
                 page.evaluate(f"window.scrollTo(0, {scroll_position});")
                 screenshot_data = page.screenshot()
                 screenshot_data_list.append(screenshot_data)
 
-            # Capture screenshots
+            capture_screenshot(0, screenshot_counter)
-            capture_screenshot(0, screenshot_counter)  # First screenshot
             screenshot_counter += 1
-            capture_screenshot(viewport_height, screenshot_counter)  # Second screenshot
+            capture_screenshot(viewport_height, screenshot_counter)
 
             browser.close()
 
-        # Store screenshot data as bytes in the state dictionary
         for screenshot_data in screenshot_data_list:
             screenshots.append(screenshot_data)
+
         state["link"] = self.url
         state['screenshots'] = screenshots
+
         return state

scrapegraphai/nodes/generate_answer_from_image_node.py

@@ -6,7 +6,7 @@ import requests
 class GenerateAnswerFromImageNode(BaseNode):
     """
     GenerateAnswerFromImageNode analyzes images from the state dictionary using the OpenAI API
-    and updates the state with the generated answers.
+    and updates the state with the consolidated answers.
     """
 
     def __init__(
@@ -19,20 +19,28 @@ class GenerateAnswerFromImageNode(BaseNode):
         super().__init__(node_name, "node", input, output, 2, node_config)
 
     def execute(self, state: dict) -> dict:
-        """Processes images from the state, generates answers, and updates the state."""
+        """
-        # Retrieve the image data from the state dictionary
+        Processes images from the state, generates answers, 
+        consolidates the results, and updates the state.
+        """
         images = state.get('screenshots', [])
-        results = []
+        analyses = []
+
+        api_key = self.node_config.get("config", {}).get("llm", {}).get("api_key", "")
+
+        supported_models = ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo"]
+
+        if self.node_config["config"]["llm"]["model"] not in supported_models:
+            raise ValueError(f"""Model '{self.node_config['config']['llm']['model']}' 
+                             is not supported. Supported models are: 
+                             {', '.join(supported_models)}.""")
 
-        # OpenAI API Key
         for image_data in images:
-            # Encode the image data to base64
             base64_image = base64.b64encode(image_data).decode('utf-8')
 
-            # Prepare API request
             headers = {
                 "Content-Type": "application/json",
-                "Authorization": f"Bearer {self.node_config.get("config").get("llm").get("api_key")}"
+                "Authorization": f"Bearer {api_key}"
             }
 
             payload = {
@@ -43,7 +51,8 @@ class GenerateAnswerFromImageNode(BaseNode):
                         "content": [
                             {
                                 "type": "text",
-                                "text": state.get("user_prompt", "Extract information from the image")
+                                "text": state.get("user_prompt", 
+                                                  "Extract information from the image")
                             },
                             {
                                 "type": "image_url",
@@ -57,18 +66,20 @@ class GenerateAnswerFromImageNode(BaseNode):
                 "max_tokens": 300
             }
 
-            # Make the API request
+            response = requests.post("https://api.openai.com/v1/chat/completions",
-            response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
+                                     headers=headers,
+                                     json=payload,
+                                     timeout=10 )
             result = response.json()
 
-            # Extract the response text
+            response_text = result.get('choices',
-            response_text = result.get('choices', [{}])[0].get('message', {}).get('content', 'No response')
+                                       [{}])[0].get('message', {}).get('content', 'No response')
+            analyses.append(response_text)
 
-            # Append the result to the results list
+        consolidated_analysis = " ".join(analyses)
-            results.append({
+
-                "analysis": response_text
+        state['answer'] = {
-            })
+            "consolidated_analysis": consolidated_analysis
+        }
 
-        # Update the state dictionary with the results
-        state['answer'] = results
         return state

tests/graphs/screenshot_scraper_test.py

@@ -0,0 +1,39 @@
+import os
+import pytest
+import json
+from scrapegraphai.graphs import ScreenshotScraperGraph
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Define a fixture for the graph configuration
+@pytest.fixture
+def graph_config():
+    """
+    Creation of the graph
+    """
+    return {
+        "llm": {
+            "api_key": os.getenv("OPENAI_API_KEY"),
+            "model": "gpt-4o",
+        },
+        "verbose": True,
+        "headless": False,
+    }
+
+def test_screenshot_scraper_graph(graph_config):
+    """
+    test
+    """
+    smart_scraper_graph = ScreenshotScraperGraph(
+        prompt="List me all the projects",
+        source="https://perinim.github.io/projects/",
+        config=graph_config
+    )
+
+    result = smart_scraper_graph.run()
+
+    assert result is not None, "The result should not be None"
+
+    print(json.dumps(result, indent=4))