Asses.ai

app.py

@@ -0,0 +1 @@
+

main.py

@@ -0,0 +1,389 @@
+# from fastapi import FastAPI, Form, HTTPException
+# import os
+# import json
+# from dotenv import load_dotenv
+# import google.generativeai as genai
+
+# app = FastAPI()
+
+# # Load environment variables
+# load_dotenv()
+
+# # Configure Gemini API
+# genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+# # Helper function to get response from the Gemini API
+# def get_gemini_response(input_text: str):
+#     """
+#     Get a response from the Gemini model based on the input prompt.
+#     """
+#     try:
+#         model = genai.GenerativeModel('gemini-pro')
+#         response = model.generate_content(input_text)
+#         return response.text
+#     except Exception as e:
+#         raise HTTPException(status_code=500, detail=f"Error generating response: {str(e)}")
+
+# # Endpoint 1: Radio Button Type Question Generation
+# @app.post("/dynamic_question_generation_radio")
+# async def dynamic_assessment_radio(
+#     knowledge_level: str = Form(...), 
+#     learning_goal: str = Form(...)
+# ):
+#     """
+#     Endpoint to dynamically generate radio button questions.
+#     """
+#     prompt = f"""
+#     You are an AI-powered assessment platform that dynamically adapts questions based on a student's performance and knowledge level.
+
+#     **Student's Knowledge Level:**
+#     {knowledge_level}
+
+#     **Learning Goal:**
+#     {learning_goal}
+
+#     **Adaptive Questions:**
+#     Generate 5 personalized radio button questions for the student based on their current knowledge level.
+#     Each question should:
+#     - Have 4 answer options for radio button selection (only one correct answer).
+#     - Provide a correct answer for evaluation.
+#     Provide output in this format:
+#     {{
+#         "questions": [
+#             {{
+#                 "question_id": 1,
+#                 "question": "What is 2+2?",
+#                 "difficulty_level": "easy",
+#                 "options": ["3", "4", "5", "6"],
+#                 "correct_answer": "4"
+#             }},
+#             ...
+#         ]
+#     }}
+#     """
+
+#     response = get_gemini_response(prompt)
+#     try:
+#         response_dict = json.loads(response)
+#     except json.JSONDecodeError:
+#         raise HTTPException(status_code=500, detail="Failed to parse the response into JSON.")
+
+#     return response_dict
+
+
+# # Endpoint 2: Checkbox Type Question Generation
+# @app.post("/dynamic_question_generation_checkbox")
+# async def dynamic_assessment_checkbox(
+#     knowledge_level: str = Form(...), 
+#     learning_goal: str = Form(...)
+# ):
+#     """
+#     Endpoint to dynamically generate checkbox questions.
+#     """
+#     prompt = f"""
+#     You are an AI-powered assessment platform that dynamically adapts questions based on a student's performance and knowledge level.
+
+#     **Student's Knowledge Level:**
+#     {knowledge_level}
+
+#     **Learning Goal:**
+#     {learning_goal}
+
+
+#     **Adaptive Questions:**
+#     Generate 5 personalized checkbox questions for the student based on their current knowledge level. 
+#     Each question should:
+#     - Have 4 answer options for checkbox selection (multiple correct answers possible).
+#     - Provide a list of correct answers for evaluation.
+#     Provide output in this format:
+#     {{
+#         "questions": [
+#             {{
+#                 "question_id": 1,
+#                 "question": "Which of the following are prime numbers?",
+#                 "difficulty_level": "medium",
+#                 "options": ["2", "4", "6", "7"],
+#                 "correct_answers": ["2", "7"]
+#             }},
+#             ...
+#         ]
+#     }}
+#     """
+
+#     response = get_gemini_response(prompt)
+#     try:
+#         response_dict = json.loads(response)
+#     except json.JSONDecodeError:
+#         raise HTTPException(status_code=500, detail="Failed to parse the response into JSON.")
+
+#     return response_dict
+
+
+# # Endpoint 3: Fill-in-the-Blank Question Generation
+# @app.post("/dynamic_question_generation_fill_in_the_blanks")
+# async def dynamic_assessment_fill_in_the_blanks(
+#     knowledge_level: str = Form(...), 
+#     learning_goal: str = Form(...)
+# ):
+#     """
+#     Endpoint to dynamically generate fill-in-the-blank questions.
+#     """
+#     prompt = f"""
+#     You are an AI-powered assessment platform that dynamically adapts questions based on a student's performance and knowledge level.
+
+#     **Student's Knowledge Level:**
+#     {knowledge_level}
+
+#     **Learning Goal:**
+#     {learning_goal}
+
+#     **Adaptive Questions:**
+#     Generate 5 personalized fill-in-the-blank questions for the student based on their current knowledge level. 
+#     Each question should:
+#     - Include a sentence with one or more blanks for the student to fill in.
+#     - Provide the correct answers for the blanks for evaluation.
+#     Provide output in this format:
+#     {{
+#         "questions": [
+#             {{
+#                 "question_id": 1,
+#                 "sentence": "The capital of France is ____.",
+#                 "difficulty_level": "easy",
+#                 "correct_answers": ["Paris"]
+#             }},
+#             ...
+#         ]
+#     }}
+#     """
+
+#     response = get_gemini_response(prompt)
+#     try:
+#         response_dict = json.loads(response)
+#     except json.JSONDecodeError:
+#         raise HTTPException(status_code=500, detail="Failed to parse the response into JSON.")
+
+#     return response_dict
+
+# print("Script is running")
+
+
+
+from fastapi import FastAPI, Form, HTTPException
+import os
+import json
+from dotenv import load_dotenv
+from io import BytesIO
+from PIL import Image
+import base64
+from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
+import torch
+
+app = FastAPI()
+
+# Load environment variables
+load_dotenv()
+
+from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
+import torch
+
+model_id = "stabilityai/stable-diffusion-2"
+scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+
+# Remove the torch_dtype argument for compatibility with CPU
+pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler)
+
+# If running on CPU, use float32 (the default)
+pipe = pipe.to("cpu")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]
+image.show()
+
+# Helper function to convert PIL image to base64 string
+def pil_to_base64(img: Image.Image) -> str:
+    buffered = BytesIO()
+    img.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
+    return img_str
+
+# Helper function to get response from the Stable Diffusion model using the new scheduler
+def generate_image_from_prompt(prompt: str):
+    """
+    Generate an image from a text prompt using Stable Diffusion with EulerDiscreteScheduler.
+    """
+    try:
+        # Generate image
+        image = pipe(prompt).images[0]
+        # Convert image to base64 string
+        return pil_to_base64(image)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error generating image: {str(e)}")
+
+# Helper function to get response from the Gemini API
+def get_gemini_response(input_text: str):
+    """
+    Get a response from the Gemini model based on the input prompt.
+    """
+    try:
+        model = genai.GenerativeModel('gemini-pro')  # Use the Gemini API
+        response = model.generate_content(input_text)
+        return response.text
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error generating response: {str(e)}")
+
+# Endpoint 1: Radio Button Type Question Generation
+@app.post("/dynamic_question_generation_radio")
+async def dynamic_assessment_radio(
+    knowledge_level: str = Form(...), 
+    learning_goal: str = Form(...)
+):
+    """
+    Endpoint to dynamically generate radio button questions.
+    """
+    prompt = f"""
+    You are an AI-powered assessment platform that dynamically adapts questions based on a student's performance and knowledge level.
+
+    **Student's Knowledge Level:**
+    {knowledge_level}
+
+    **Learning Goal:**
+    {learning_goal}
+
+    **Adaptive Questions:**
+    Generate 5 personalized radio button questions for the student based on their current knowledge level.
+    Each question should:
+    - Have 4 answer options for radio button selection (only one correct answer).
+    - Provide a correct answer for evaluation.
+    Provide output in this format:
+    {{
+        "questions": [
+            {{
+                "question_id": 1,
+                "question": "What is 2+2?",
+                "difficulty_level": "easy",
+                "options": ["3", "4", "5", "6"],
+                "correct_answer": "4"
+            }},
+            ...
+        ]
+    }}
+    """
+
+    # Generate the questions using Gemini
+    response = get_gemini_response(prompt)
+    try:
+        response_dict = json.loads(response)
+    except json.JSONDecodeError:
+        raise HTTPException(status_code=500, detail="Failed to parse the response into JSON.")
+
+    # Generate an image related to the generated questions using Stable Diffusion
+    image_prompt = f"Create an educational image related to {learning_goal} for students at {knowledge_level} level."
+    image_base64 = generate_image_from_prompt(image_prompt)
+
+    return {"questions": response_dict, "image_base64": image_base64}
+
+# Endpoint 2: Checkbox Type Question Generation
+@app.post("/dynamic_question_generation_checkbox")
+async def dynamic_assessment_checkbox(
+    knowledge_level: str = Form(...), 
+    learning_goal: str = Form(...)
+):
+    """
+    Endpoint to dynamically generate checkbox questions.
+    """
+    prompt = f"""
+    You are an AI-powered assessment platform that dynamically adapts questions based on a student's performance and knowledge level.
+
+    **Student's Knowledge Level:**
+    {knowledge_level}
+
+    **Learning Goal:**
+    {learning_goal}
+
+    **Adaptive Questions:**
+    Generate 5 personalized checkbox questions for the student based on their current knowledge level. 
+    Each question should:
+    - Have 4 answer options for checkbox selection (multiple correct answers possible).
+    - Provide a list of correct answers for evaluation.
+    Provide output in this format:
+    {{
+        "questions": [
+            {{
+                "question_id": 1,
+                "question": "Which of the following are prime numbers?",
+                "difficulty_level": "medium",
+                "options": ["2", "4", "6", "7"],
+                "correct_answers": ["2", "7"]
+            }},
+            ...
+        ]
+    }}
+    """
+
+    # Generate the questions using Gemini
+    response = get_gemini_response(prompt)
+    try:
+        response_dict = json.loads(response)
+    except json.JSONDecodeError:
+        raise HTTPException(status_code=500, detail="Failed to parse the response into JSON.")
+
+    # Generate an image related to the generated questions using Stable Diffusion
+    image_prompt = f"Create an educational image related to {learning_goal} for students at {knowledge_level} level."
+    image_base64 = generate_image_from_prompt(image_prompt)
+
+    return {"questions": response_dict, "image_base64": image_base64}
+
+# Endpoint 3: Fill-in-the-Blank Question Generation
+@app.post("/dynamic_question_generation_fill_in_the_blanks")
+async def dynamic_assessment_fill_in_the_blanks(
+    knowledge_level: str = Form(...), 
+    learning_goal: str = Form(...)
+):
+    """
+    Endpoint to dynamically generate fill-in-the-blank questions.
+    """
+    prompt = f"""
+    You are an AI-powered assessment platform that dynamically adapts questions based on a student's performance and knowledge level.
+
+    **Student's Knowledge Level:**
+    {knowledge_level}
+
+    **Learning Goal:**
+    {learning_goal}
+
+    **Adaptive Questions:**
+    Generate 5 personalized fill-in-the-blank questions for the student based on their current knowledge level. 
+    Each question should:
+    - Include a sentence with one or more blanks for the student to fill in.
+    - Provide the correct answers for the blanks for evaluation.
+    Provide output in this format:
+    {{
+        "questions": [
+            {{
+                "question_id": 1,
+                "sentence": "The capital of France is ____. ",
+                "difficulty_level": "easy",
+                "correct_answers": ["Paris"]
+            }},
+            ...
+        ]
+    }}
+    """
+
+    # Generate the questions using Gemini
+    response = get_gemini_response(prompt)
+    try:
+        response_dict = json.loads(response)
+    except json.JSONDecodeError:
+        raise HTTPException(status_code=500, detail="Failed to parse the response into JSON.")
+
+    # Generate an image related to the generated questions using Stable Diffusion
+    image_prompt = f"Create an educational image related to {learning_goal} for students at {knowledge_level} level."
+    image_base64 = generate_image_from_prompt(image_prompt)
+
+    return {"questions": response_dict, "image_base64": image_base64}
+
+# Start the FastAPI app
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)