Create app.py

app.py

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+import sympy as sp
+import gradio as gr
+import speech_recognition as sr
+from transformers import pipeline
+from gtts import gTTS
+import os
+import mediapipe as mp
+import cv2
+import numpy as np
+
+# Load the ASR model
+asr_pipeline = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
+
+# Mediapipe setup for hand tracking (sign language recognition)
+mp_hands = mp.solutions.hands
+hands = mp_hands.Hands(static_image_mode=False, max_num_hands=1, min_detection_confidence=0.5)
+mp_drawing = mp.solutions.drawing_utils
+
+# Sign language processing function
+def process_sign_language(image):
+    """
+    Process a hand gesture image for basic recognition.
+    """
+    try:
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        results = hands.process(image_rgb)
+        
+        if not results.multi_hand_landmarks:
+            return "No hand detected."
+        
+        landmarks = results.multi_hand_landmarks[0]
+        coords = [(lm.x, lm.y) for lm in landmarks.landmark]
+        
+        # A placeholder example: Gesture detection logic
+        thumb_tip = coords[4]
+        index_tip = coords[8]
+        distance = np.linalg.norm(np.array(thumb_tip) - np.array(index_tip))
+
+        # Simple threshold-based recognition (customize as needed)
+        if distance < 0.05:
+            return "Gesture: 'Thumb and index touching' detected (e.g., OK gesture)."
+        else:
+            return "Gesture detected, but not recognized."
+
+    except Exception as e:
+        return f"Error in sign language processing: {e}"
+
+# Function to process calculus queries
+def process_calculus(query):
+    try:
+        expr = sp.sympify(query)
+        derivative = sp.diff(expr)
+        integral = sp.integrate(expr)
+        return f"Derivative: {derivative}\nIntegral: {integral}"
+    except Exception as e:
+        return f"Error: Unable to process the query. Details: {e}"
+
+# Voice-to-text processing
+def voice_to_text(audio_file):
+    try:
+        transcription = asr_pipeline(audio_file)["text"]
+        return transcription
+    except Exception as e:
+        return f"Error in voice recognition: {e}"
+
+# Text-to-speech function
+def text_to_speech(response):
+    try:
+        tts = gTTS(response)
+        tts.save("response.mp3")
+        os.system("start response.mp3" if os.name == "nt" else "afplay response.mp3")
+    except Exception as e:
+        return f"Error in generating speech: {e}"
+
+# Main calculator function
+def calculator(input_audio=None, input_text=None, input_image=None):
+    if input_audio is not None:
+        query = voice_to_text(input_audio)
+    elif input_image is not None:
+        query = process_sign_language(input_image)
+    elif input_text:
+        query = input_text
+    else:
+        return "No valid input provided."
+
+    response = process_calculus(query)
+    text_to_speech(response)
+    return response
+
+# Gradio interface setup
+interface = gr.Interface(
+    fn=calculator,
+    inputs=[
+        gr.Audio(source="microphone", optional=True, label="Voice Input"),
+        gr.Textbox(label="Text Input (Math Expression)", optional=True),
+        gr.Image(source="webcam", optional=True, label="Sign Language Input"),
+    ],
+    outputs="text",
+    title="Accessible Calculus Solver",
+    description="Solve calculus problems using voice, text, or sign language input. Designed for accessibility.",
+)
+
+if __name__ == "__main__":
+    interface.launch()