Update app.py

app.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+import google.generativeai as genai
+from sympy import sympify, solve
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Initialize Google Generative AI
+def initialize_genai():
+    api_key = os.getenv("GOOGLE_API_KEY")
+    if not api_key:
+        raise ValueError("Google API Key not found in environment variables.")
+    genai.configure(api_key=api_key)
+
+def create_prompt(image):
+    # Adjust the prompt based on how the model expects the input
+    return "Analyze the following image of an equation. Recognize and solve the equation. Image:"
+
+def recognize_equation_with_genai(image):
+    try:
+        # Convert image to text using Google Generative AI with a prompt template
+        prompt = create_prompt(image)
+        response = genai.text_detect(image, prompt=prompt)
+        recognized_text = response.get('text', '')
+        return recognized_text.strip()
+    except Exception as e:
+        return f"Error recognizing text: {str(e)}"
+
+def solve_equation(equation):
+    try:
+        expr = sympify(equation)
+        solutions = solve(expr)
+        return str(solutions)
+    except Exception as e:
+        return f"Error solving equation: {str(e)}"
+
+def process_frame(frame):
+    # Convert frame to grayscale
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    _, thresholded = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
+
+    # Convert OpenCV image to PIL image
+    image = Image.fromarray(thresholded)
+
+    # Recognize and solve the equation
+    recognized_equation = recognize_equation_with_genai(image)
+    solutions = solve_equation(recognized_equation)
+    
+    return recognized_equation, solutions, image
+
+def main():
+    # Initialize Google Generative AI
+    initialize_genai()
+    
+    with gr.Blocks() as demo:
+        gr.Markdown("## Virtual Math Calculator with Google Generative AI")
+        
+        with gr.Row():
+            video_input = gr.Video(source="webcam", type="numpy", label="Record your video")
+            output_text = gr.Textbox(label="Recognized Equation")
+            output_solutions = gr.Textbox(label="Solution")
+            output_image = gr.Image(label="Captured Image")
+
+        def process_video(video):
+            frame = video[0]  # Take the first frame from the video
+            recognized_equation, solutions, image = process_frame(frame)
+            return recognized_equation, solutions, image
+
+        video_input.change(process_video, inputs=video_input, outputs=[output_text, output_solutions, output_image])
+
+    demo.launch()
+
+if __name__ == "__main__":
+    main()