| | import streamlit as st |
| | from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering |
| | from PIL import Image |
| | import easyocr |
| | import os |
| | from groq import Groq, APIConnectionError, AuthenticationError |
| |
|
| | |
| | def extract_text_from_image(image): |
| | reader = easyocr.Reader(['en']) |
| | result = reader.readtext(image) |
| | extracted_text = " ".join([detection[1] for detection in result]) |
| | return extracted_text |
| |
|
| | |
| | @st.cache_resource |
| | def load_qa_model(): |
| | model_name = "distilbert/distilbert-base-cased-distilled-squad" |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
| | nlp = pipeline('question-answering', model=model, tokenizer=tokenizer) |
| | return nlp |
| |
|
| | def answer_question(context, question, qa_model): |
| | result = qa_model({'question': question, 'context': context}) |
| | return result['answer'] |
| |
|
| | |
| | def groq_chat(prompt): |
| | try: |
| | client = Groq(api_key=os.environ.get("GROQ_API_KEY")) |
| | chat_completion = client.chat.completions.create( |
| | messages=[{"role": "user", "content": prompt}], |
| | model="llama-3.3-70b-versatile", |
| | ) |
| | return chat_completion.choices[0].message.content |
| | except APIConnectionError as e: |
| | return f"Groq API Connection Error: {e}" |
| | except AuthenticationError as e: |
| | return f"Groq API Authentication Error: {e}" |
| | except Exception as e: |
| | return f"General Groq API Error: {e}" |
| |
|
| | |
| | def main(): |
| | st.title("Image Text & Question Answering Chatbot") |
| |
|
| | uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) |
| |
|
| | if uploaded_file is not None: |
| | image = Image.open(uploaded_file) |
| | st.image(image, caption="Uploaded Image", use_container_width=True) |
| |
|
| | if st.button("Extract Text and Enable Question Answering"): |
| | with st.spinner("Extracting text..."): |
| | extracted_text = extract_text_from_image(image) |
| | st.write("Extracted Text:") |
| | st.write(extracted_text) |
| |
|
| | qa_model = load_qa_model() |
| |
|
| | question = st.text_input("Ask a question about the image text:") |
| | if st.button("Answer"): |
| | if question: |
| | with st.spinner("Answering..."): |
| | answer = answer_question(extracted_text, question, qa_model) |
| | st.write("Answer:", answer) |
| | else: |
| | st.warning("Please enter a question.") |
| |
|
| | |
| | st.subheader("General Chat (Powered by Groq)") |
| | groq_prompt = st.text_input("Enter your message:") |
| | if st.button("Send"): |
| | if groq_prompt: |
| | with st.spinner("Generating response..."): |
| | groq_response = groq_chat(groq_prompt) |
| | st.write("Response:", groq_response) |
| | else: |
| | st.warning("Please enter a message.") |
| |
|
| | if __name__ == "__main__": |
| | main() |
