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Update app.py
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app.py
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import streamlit as st
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import cv2
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import openai
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import numpy as np
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import mediapipe as mp
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import tempfile
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import os
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# Set your OpenAI API key (or get it from user input)
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openai.api_key = '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'
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# Initialize MediaPipe Hands
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mp_hands = mp.solutions.hands
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mp_drawing = mp.solutions.drawing_utils
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def detect_gesture(hand_landmarks):
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# Example gesture recognition logic
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thumb_tip = hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_TIP].y
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index_finger_tip = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y
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middle_finger_tip = hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_TIP].y
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ring_finger_tip = hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_TIP].y
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pinky_tip = hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_TIP].y
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# Conditions for different gestures
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if index_finger_tip < thumb_tip and middle_finger_tip > index_finger_tip:
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return "draw"
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elif middle_finger_tip < index_finger_tip and ring_finger_tip > middle_finger_tip:
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return "navigate"
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elif thumb_tip < index_finger_tip and pinky_tip > ring_finger_tip:
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return "reset"
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elif pinky_tip < ring_finger_tip:
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return "submit"
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return None
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def solve_math_problem_with_openai(problem):
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response = openai.Completion.create(
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engine="gpt-3.5-turbo", # Or another model of your choice
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prompt=f"Solve this math problem: {problem}",
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max_tokens=150
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)
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solution = response.choices[0].text.strip()
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return solution
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def main():
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st.title("
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# Create a placeholder for the video feed
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stframe = st.empty()
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st.error("Failed to open the camera. Please check your camera settings and permissions.")
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return
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#
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# Process the frame with MediaPipe hands
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results = hands.process(frame_rgb)
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# Draw hand landmarks on the frame
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if results.multi_hand_landmarks:
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for hand_landmarks in results.multi_hand_landmarks:
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mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
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gesture = detect_gesture(hand_landmarks)
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if gesture:
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st.write(f"Detected Gesture: {gesture}")
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# Display the frame in the Streamlit app
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stframe.image(frame, channels="BGR")
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# Break the loop if the stop button is pressed
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if st.button('Stop'):
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st.write("Stopping the camera...")
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break
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# Release the camera resource
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cap.release()
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import streamlit as st
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import cv2
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import numpy as np
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def main():
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st.title("Camera Access with Streamlit")
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# Create a placeholder for the video feed
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stframe = st.empty()
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st.error("Failed to open the camera. Please check your camera settings and permissions.")
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return
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while True:
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ret, frame = cap.read()
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if not ret:
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st.error("Failed to grab frame from the camera.")
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break
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# Flip the frame horizontally for natural interaction
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frame = cv2.flip(frame, 1)
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# Convert the frame to RGB
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# Display the frame in the Streamlit app
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stframe.image(frame_rgb, channels="RGB")
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# Break the loop if the stop button is pressed
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if st.button('Stop'):
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st.write("Stopping the camera...")
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break
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# Release the camera resource
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cap.release()
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