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app.py

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+
+import streamlit as st
+import google.generativeai as genai
+import PyPDF2 as pdf
+import pandas as pd
+import tempfile
+import openpyxl
+from openpyxl.utils import get_column_letter
+from PIL import Image
+import easyocr
+import numpy as np  # Make sure to import numpy
+
+# Configure API key
+genai.configure(api_key="AIzaSyDm0pOQKmzLMPU9omEOIr8nsFdGld9cuG8")
+
+# Initialize the OCR reader
+reader = easyocr.Reader(['en'])
+
+# Function to get response from Generative AI model
+def get_gemini_response(input):
+    model = genai.GenerativeModel('gemini-pro')
+    response = model.generate_content(input)
+    return response
+
+# Convert PDF to text
+def input_pdf_text(uploaded_file):
+    reader_pdf = pdf.PdfReader(uploaded_file)
+    text = ""
+    for page in range(len(reader_pdf.pages)):
+        page = reader_pdf.pages[page]
+        text += str(page.extract_text())
+    return text
+
+# Extract text from images using EasyOCR
+def input_image_text(uploaded_file):
+    # Open the image using PIL
+    image = Image.open(uploaded_file)
+    # Convert the image to a NumPy array
+    image_np = np.array(image)
+    # Perform OCR on the image
+    text = reader.readtext(image_np, detail=0)  # Extract text as a list of strings
+    return ' '.join(text)  # Join the extracted text into a single string
+
+# Extract information based on each criterion
+def extract_information_per_criterion(text, criteria_list):
+    extracted_data = {}
+    for criterion in criteria_list:
+        prompt = f"Please analyze the following text and extract the key points related to '{criterion}'. Provide the output as a simple string without any extra formatting or labels. Here’s the text:\n{text}"
+        response = get_gemini_response(prompt)
+        extracted_text = response.candidates[0].content.parts[0].text.strip().replace('*', '')  # Remove asterisks
+        extracted_data[criterion] = extracted_text
+    return extracted_data
+
+
+
+# Store extracted information into a DataFrame
+def information_to_df(extracted_data, sr_no):
+    data = {criterion: [extracted_data.get(criterion, "")] for criterion in extracted_data}
+    df = pd.DataFrame(data)
+    df.insert(0, "Sr. No", sr_no)
+    return df
+
+# Adjust Excel columns to fit content
+def adjust_excel_columns(writer, df):
+    worksheet = writer.sheets['Sheet1']
+    for idx, col in enumerate(df.columns, 1):  # 1-indexed.
+        max_length = max(df[col].astype(str).map(len).max(), len(col))
+        worksheet.column_dimensions[get_column_letter(idx)].width = max_length + 2
+
+# Streamlit App
+st.title("File Information Extractor")
+st.text("Upload PDFs, JPGs, or PNGs and specify criteria for information extraction")
+
+uploaded_files = st.file_uploader("Upload your files (PDF, JPG, PNG)", type=["pdf", "jpg", "png"], accept_multiple_files=True)
+
+if uploaded_files:
+    user_input = st.text_area("Enter the criteria for extracting information, separated by commas.")
+
+    if user_input:
+        criteria_list = [criterion.strip() for criterion in user_input.split(',')]  # Split and clean criteria
+        all_dfs = []
+
+        for i, uploaded_file in enumerate(uploaded_files, start=1):
+            # Determine file type and handle accordingly
+            if uploaded_file.type == "application/pdf":
+                text = input_pdf_text(uploaded_file)
+                extracted_data = extract_information_per_criterion(text, criteria_list)
+
+                st.subheader(f"Extracted Information from PDF File {i}")
+                st.write(extracted_data)
+
+                df = information_to_df(extracted_data, i)
+                all_dfs.append(df)
+
+            elif uploaded_file.type in ["image/jpeg", "image/png"]:
+                text = input_image_text(uploaded_file)  # Extract text from image using OCR
+                extracted_data = extract_information_per_criterion(text, criteria_list)
+
+                st.subheader(f"Extracted Information from Image File {i}")
+                st.write(extracted_data)
+
+                df = information_to_df(extracted_data, i)
+                all_dfs.append(df)
+
+        # Combine all DataFrames into one
+        combined_df = pd.concat(all_dfs, ignore_index=True)
+
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx") as tmp_file:
+            with pd.ExcelWriter(tmp_file.name, engine='openpyxl') as writer:
+                combined_df.to_excel(writer, index=False)
+                adjust_excel_columns(writer, combined_df)
+
+            excel_path = tmp_file.name
+
+        with open(excel_path, "rb") as file:
+            st.download_button(
+                label="Download Extracted Information as Excel",
+                data=file,
+                file_name="extracted_information.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            )

requirements.txt

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+streamlit
+PyPDF2
+google.generativeai
+python-dotenv
+openpyxl
+numpy
+pandas
+easyocr