Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering
+from PIL import Image
+import easyocr
+import os
+from groq import Groq, APIConnectionError, AuthenticationError
+
+# OCR Function
+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
+
+# Question Answering Function (DistilBERT)
+@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']
+
+# Groq API Function
+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}"
+
+# Streamlit App
+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.session_state.extracted_text = extracted_text  # Store in session state
+                st.write("Extracted Text:")
+                st.write(st.session_state.extracted_text)
+
+        if "extracted_text" in st.session_state:  # Check if extracted_text is in session state
+            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(st.session_state.extracted_text, question, qa_model)
+                        st.write("Answer:", answer)
+                else:
+                    st.warning("Please enter a question.")
+
+    # Groq Chat Section
+    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()