Create app.py

app.py

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+import os
+import streamlit as st
+from PIL import Image
+# import pytesseract
+from paddleocr import PaddleOCR, draw_ocr
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain_groq import ChatGroq
+import numpy as np
+# import pytesseract
+# import easyocr
+
+
+
+GROQ_API_KEY = os.environ['GROQ_API_KEY '] 
+# llm = ChatGroq(temperature=0, groq_api_key=GROQ_API_KEY, model_name="mixtral-8x7b-32768")
+llm = ChatGroq(temperature=0, groq_api_key=GROQ_API_KEY, model_name="llama3-groq-70b-8192-tool-use-preview")
+
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
+# Function to extract text from an image using OCR
+def extract_text_from_image(image):
+    try:
+        # Convert PIL Image to NumPy array
+        image_np = np.array(image)
+        
+        # Perform OCR using PaddleOCR
+        results = ocr.ocr(image_np, cls=True)
+        
+        # Extract text from results
+        extracted_text = ""
+        for line in results:
+            for word_info in line:
+                extracted_text += word_info[1][0] + " "
+        
+        return extracted_text.strip() if extracted_text else "No text found in the image."
+   
+    except Exception as e:
+        return f"Error processing image: {e}"
+    # try:
+    #     # Use pytesseract to do OCR on the image
+    #     # text = pytesseract.image_to_string(image)
+    #     text = pytesseract.image_to_string(image, lang='eng', config='--psm 7')
+    #     return text.strip()
+    # except Exception as e:
+    #     return f"Error processing image: {e}"
+
+# Function to solve a math problem using Groq API and Mistral LLM
+def solve_math_problem_with_groq(problem_text):
+    try:
+        # Initialize the Groq LLM with the Mistral model
+       
+        # Create a prompt template for solving the math problem
+        prompt_template = PromptTemplate(
+            input_variables=["problem"],
+            template="Solve the following math problem:\n\n{problem}\n\n"
+        )
+
+        # Create an LLM chain with the prompt template
+        llm_chain = LLMChain(
+            llm=llm,
+            prompt=prompt_template
+        )
+
+        # Run the LLM chain with the problem text
+        solution = llm_chain.run(problem=problem_text)
+        return solution.strip()
+    except Exception as e:
+        return f"Error solving the problem: {e}"
+
+# Streamlit app
+def main():
+    st.title("Math Solver using ChatGroq, Mistral, and LangChain")
+    st.write('Developed by Ravi Shankar Prasad- https://www.linkedin.com/in/ravi-shankar-prasad-371825101/')
+
+    # Input method selection
+    input_method = st.radio("Select input method:", ("Upload Image", "Enter Text"))
+
+    problem_text = ""
+
+    if input_method == "Upload Image":
+        # Image upload
+        uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
+        if uploaded_file is not None:
+            # Display uploaded image
+            image = Image.open(uploaded_file)
+            st.image(image, caption='Uploaded Image', use_column_width=True)
+            # Extract text from image
+            problem_text = extract_text_from_image(image)
+            if problem_text:
+                st.write("Extracted Text:", problem_text)
+            else:
+                st.write("No text found in the image. Please upload a clearer image.")
+    else:
+        # Text input
+        problem_text = st.text_area("Enter math problem:")
+
+    if problem_text:
+        if st.button("Solve"):
+            # Solve the math problem using Groq and Mistral
+            solution = solve_math_problem_with_groq(problem_text)
+            st.write("Solution:", solution)
+
+if __name__ == "__main__":
+    main()