Merge pull request #247 from elder-plinius/master

playground/demos/education/education.py

@@ -0,0 +1,56 @@
+import os
+from dotenv import load_dotenv
+from swarms.models import OpenAIChat
+from swarms.models.stable_diffusion import StableDiffusion
+from swarms.structs import Agent, SequentialWorkflow
+import swarms.prompts.education as edu_prompts
+
+# Load environment variables
+load_dotenv()
+api_key = os.getenv("OPENAI_API_KEY")
+stability_api_key = os.getenv("STABILITY_API_KEY")
+
+# Initialize language model
+llm = OpenAIChat(openai_api_key=api_key, temperature=0.5, max_tokens=3000)
+
+# Initialize Stable Diffusion
+sd_api = StableDiffusion(api_key=stability_api_key)
+
+# User preferences (can be dynamically set in a real application)
+user_preferences = {
+    "subjects": "Cognitive Architectures",
+    "learning_style": "Visual",
+    "challenge_level": "Moderate"
+}
+
+# Formatted prompts from user preferences
+curriculum_prompt = edu_prompts.CURRICULUM_DESIGN_PROMPT.format(**user_preferences)
+interactive_prompt = edu_prompts.INTERACTIVE_LEARNING_PROMPT.format(**user_preferences)
+sample_prompt = edu_prompts.SAMPLE_TEST_PROMPT.format(**user_preferences)
+image_prompt = edu_prompts.IMAGE_GENERATION_PROMPT.format(**user_preferences)
+
+# Initialize agents for different educational tasks
+curriculum_agent = Agent(llm=llm, max_loops=1, sop=curriculum_prompt)
+interactive_learning_agent = Agent(llm=llm, max_loops=1, sop=interactive_prompt)
+sample_lesson_agent = Agent(llm=llm, max_loops=1, sop=sample_prompt)
+
+# Create Sequential Workflow
+workflow = SequentialWorkflow(max_loops=1)
+
+# Add tasks to workflow with personalized prompts
+workflow.add(curriculum_agent, "Generate a curriculum")
+workflow.add(interactive_learning_agent, "Generate an interactive lesson")
+workflow.add(sample_lesson_agent, "Generate a practice test")
+
+# Execute the workflow for text-based tasks
+workflow.run()
+
+# Generate an image using Stable Diffusion
+image_result = sd_api.run(image_prompt)
+
+# Output results for each task
+for task in workflow.tasks:
+    print(f"Task Description: {task.description}\nResult: {task.result}\n")
+
+# Output image result
+print(f"Image Generation Task: Generate an image for the interactive lesson\nResult: {image_result}")

swarms/models/gpt4_vision_api.py

@@ -117,19 +117,13 @@ class GPT4VisionAPI:
         pass
 
     # Function to handle vision tasks
-    def run(
-        self,
-        task: Optional[str] = None,
-        img: Optional[str] = None,
-        *args,
-        **kwargs,
-    ):
+    def run(self, img, task):
         """Run the model."""
         try:
             base64_image = self.encode_image(img)
             headers = {
                 "Content-Type": "application/json",
-                "Authorization": f"Bearer {openai_api_key}",
+                "Authorization": f"Bearer {self.openai_api_key}",
             }
             payload = {
                 "model": self.model_name,
@@ -154,28 +148,24 @@ class GPT4VisionAPI:
                 "max_tokens": self.max_tokens,
             }
             response = requests.post(
-                self.openai_proxy,
-                headers=headers,
-                json=payload,
+                self.openai_proxy, headers=headers, json=payload
             )
 
             out = response.json()
-            content = out["choices"][0]["message"]["content"]
-
-            if self.streaming_enabled:
-                content = self.stream_response(content)
+            if "choices" in out and out["choices"]:
+                content = (
+                    out["choices"][0]
+                    .get("message", {})
+                    .get("content", None)
+                )
+                return content
             else:
-                pass
-
-            if self.beautify:
-                content = colored(content, "cyan")
-                print(content)
-            else:
-                print(content)
+                print("No valid response in 'choices'")
+                return None
 
         except Exception as error:
             print(f"Error with the request: {error}")
-            raise error
+            return None
 
     def video_prompt(self, frames):
         """

swarms/prompts/education.py

@@ -0,0 +1,35 @@
+user_preferences = {
+    "subjects": "AI Cognitive Architectures",
+    "learning_style": "Visual",
+    "challenge_level": "Moderate",
+}
+
+# Extracting individual preferences
+subjects = user_preferences["subjects"]
+learning_style = user_preferences["learning_style"]
+challenge_level = user_preferences["challenge_level"]
+
+
+# Curriculum Design Prompt
+CURRICULUM_DESIGN_PROMPT = f"""
+Develop a semester-long curriculum tailored to student interests in {subjects}. Focus on incorporating diverse teaching methods suitable for a {learning_style} learning style. 
+The curriculum should challenge students at a {challenge_level} level, integrating both theoretical knowledge and practical applications. Provide a detailed structure, including 
+weekly topics, key objectives, and essential resources needed.
+"""
+
+# Interactive Learning Session Prompt
+INTERACTIVE_LEARNING_PROMPT = f"""
+Basedon the curriculum, generate an interactive lesson plan for a student of {subjects} that caters to a {learning_style} learning style. Incorporate engaging elements and hands-on activities.
+"""
+
+# Sample Lesson Prompt
+SAMPLE_TEST_PROMPT = f"""
+Create a comprehensive sample test for the first week of the {subjects} curriculum, tailored for a {learning_style} learning style and at a {challenge_level} challenge level.
+"""
+
+# Image Generation for Education Prompt
+IMAGE_GENERATION_PROMPT = f"""
+Develop a stable diffusion prompt for an educational image/visual aid that align with the {subjects} curriculum, specifically designed to enhance understanding for students with a {learning_style} 
+learning style. This might include diagrams, infographics, and illustrative representations to simplify complex concepts. Ensure you output a 10/10 descriptive image generation prompt only.
+"""
+