working locally

.gitignore

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+.env

app.py

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+import streamlit as st
+
+from summarize import Summarizer
+
+def main():
+    st.title("Text Extractor and Summarizer")
+    
+    summarizer = Summarizer()
+    summarizer.run_app()
+
+main()

requirements.txt

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+streamlit
+pdfplumber
+pillow
+pytesseract
+transformers
+torch
+groq
+python-dotenv

summarize.py

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+import streamlit as st
+import pdfplumber
+from PIL import Image
+import pytesseract
+#from transformers import pipeline
+import io
+
+import os
+from dotenv import load_dotenv
+
+# groq
+from groq import Groq
+
+# SwedishBeagle-dare
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+class Summarizer:
+
+    def __init__(self, model = "groq"):
+        self.model = model
+        self.client = self.load_groq()
+
+    def run_app(self):
+        uploaded_file = st.file_uploader("Upload an Image or PDF", type=["jpg", "jpeg", "png", "pdf"])
+    
+        if uploaded_file is not None:
+            if uploaded_file.type == "application/pdf":
+                with st.spinner("Extracting text from PDF..."):
+                    text = self.extract_text_from_pdf(uploaded_file)
+            else:
+                image = Image.open(uploaded_file)
+                with st.spinner("Extracting text from image..."):
+                    text = self.extract_text_from_image(image)
+    
+            if text:
+                with st.spinner("Summarizing text..."):
+                    summary = self.summarize_using_groq(text)
+                st.subheader("Summary")
+                st.write(summary)
+    
+            st.subheader("Extracted Text")
+            st.write(text)
+
+
+    # Function to extract text from an image
+    def extract_text_from_image(self, image):
+        text = pytesseract.image_to_string(image)
+        return text
+    
+    # Function to extract text from a PDF
+    def extract_text_from_pdf(self, pdf):
+        text = ""
+        with pdfplumber.open(pdf) as pdf_file:
+            for page in pdf_file.pages:
+                text += page.extract_text()
+        return text
+    
+    # Function to summarize text
+    #def summarize_text(self, text):
+    #    summarizer = pipeline("summarization")
+    #    summary = summarizer(text, max_length=150, min_length=30, do_sample=False)
+    #    return summary[0]['summary_text']
+    
+    def load_groq(self):
+        load_dotenv()
+    
+        GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+    
+        client = Groq(
+            api_key=GROQ_API_KEY
+        )
+    
+        return client
+
+    def summarize_using_groq(self, text):
+        chat_completion = self.client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You summarize texts that the users sends"
+                },
+                {
+                    "role": "user",
+                    "content": text,
+                }
+            ],
+            model="mixtral-8x7b-32768",
+        )
+    
+        return chat_completion.choices[0].message.content
+    
+    def summarize_using_swedishbeagle(self, text):
+        # https://huggingface.co/FredrikBL/SwedishBeagle-dare
+
+        model = "FredrikBL/SwedishBeagle-dare"
+        messages = [
+            {
+                "role": "system", 
+                "content": "You summarize texts that the users sends"
+            }, 
+            {
+                "role": "user", 
+                "content": text
+            }
+        ]
+
+        tokenizer = AutoTokenizer.from_pretrained(model)
+        prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        pipeline = transformers.pipeline(
+            "text-generation",
+            model=model,
+            torch_dtype=torch.float16,
+            device_map="auto",
+        )
+
+        outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+        return outputs[0]["generated_text"]
+
+    def summarize(self, text):
+        if(self.model == "groq"):
+            return self.summarize_using_groq(text)
+        elif(self.model == "SwedishBeagle-dare"):
+            return self.summarize_using_swedishbeagle(text)
+        
+
+# Streamlit app
+def main():
+    # Models:
+    # - groq
+    # - SwedishBeagle-dare
+    summarizer = Summarizer(model="SwedishBeagle-dare")
+    summarizer.run_app()
+
+if __name__ == "__main__":
+    main()