Added Crime Lens Api

.gitignore

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+venv/
+venv1/
+.env

app.py

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+from dotenv import load_dotenv
+
+load_dotenv()  # take environment variables from .env.
+
+import streamlit as st
+import os
+from PIL import Image
+import google.generativeai as genai
+import json
+
+os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+## Function to load OpenAI model and get responses
+def get_gemini_response(input, image, prompt):
+    model = genai.GenerativeModel('gemini-pro-vision')
+    response = model.generate_content([input, image[0], prompt])
+    return response.text
+
+def input_image_setup(uploaded_file):
+    # Check if a file has been uploaded
+    if uploaded_file is not None:
+        # Read the file into bytes
+        bytes_data = uploaded_file.getvalue()
+
+        image_parts = [
+            {
+                "mime_type": uploaded_file.type,  # Get the mime type of the uploaded file
+                "data": bytes_data
+            }
+        ]
+        return image_parts
+    else:
+        raise FileNotFoundError("No file uploaded")
+
+##initialize our streamlit app
+
+st.set_page_config(page_title="Gemini Image Demo")
+
+st.header("Criminal Case Filing System OCR")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["pdf", "jpg", "jpeg", "png"])
+image = ""
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image.", use_column_width=True)
+
+submit = st.button("Extract data")
+
+## If ask button is clicked
+if submit:
+    input_prompt = """
+                      Extract the data of FIR No, Date and Time of FIR, Occurrence of offence, Type of information, Place of occurrence, Complaint / Victim's name
+                  """
+    image_data = input_image_setup(uploaded_file)
+    response = get_gemini_response(input_prompt, image_data, input_prompt)
+    st.subheader("The Response is")
+    print("Respons")
+    # Extracting field data
+    field_data = {
+        "FIR No": "",
+        "Date and Time of FIR": "",
+        "Occurrence of offence": "",
+        "Type of information": "",
+        "Place of occurrence": "",
+        "Victim": ""
+    }
+
+    # Parse response to extract field data
+    lines = response.split("\n")
+    for line in lines:
+        for field in field_data.keys():
+            if field in line:
+                value = line.split(":")[1].strip()
+                field_data[field] = value
+
+    # Display field data in a table
+    st.table(field_data)
+
+    # Save field data as JSON
+    json_filename = "field_data.json"
+    with open(json_filename, "w") as json_file:
+        json.dump(field_data, json_file)
+
+    # Add a download button for the JSON file
+    st.download_button(
+        label="Download JSON",
+        data=json.dumps(field_data),
+        file_name="field_data.json",
+        mime="application/json"
+    )
+
+     # Assuming your privacy ops portal URL is localhost:3000
+    portal_url = "http://localhost:3000/new-case?"
+    print(portal_url)  # Print portal URL for debugging
+
+    cleaned_field_data = {key.replace(' ', ''): value.replace(' ', '_') for key, value in field_data.items()}
+
+    # Construct the URL with cleaned_field_data
+    url_params = "&".join([f"{key}={value}" for key, value in cleaned_field_data.items()])
+    full_url = portal_url + url_params
+    st.write("Click on the below link to open the PrivacyOps Portal with the extracted data:")
+    st.write(full_url)
+

field_data.json

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+{"FIR No": "1980", "Date and Time of FIR": "10/03/2018 10", "Occurrence of offence": "04/03/2018 18", "Type of information": "Written", "Place of occurrence": "PS Raiboga", "Victim": "Erick"}

requirements.txt

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+streamlit
+google-generativeai
+python-dotenv
+langchain
+PyPDF2
+chromadb
+faiss-cpu
+Pillow