Create abc.txt

abc.txt

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+import streamlit as st
+import os
+import glob
+import base64
+import json
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+from langchain_openai import ChatOpenAI
+from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.output_parsers import JsonOutputParser
+from langchain_core.runnables import chain
+from PIL import Image as PILImage
+from io import BytesIO
+
+# Streamlit title
+st.title("Vehicle Information Extraction from Images")
+
+# Prompt user for OpenAI API key
+openai_api_key = st.text_input("Enter your OpenAI API Key:", type="password")
+
+# Set the OpenAI API key if provided
+if openai_api_key:
+    os.environ["OPENAI_API_KEY"] = openai_api_key
+
+# Vehicle class (same as in the original code)
+class Vehicle(BaseModel):
+    Type: str = Field(..., examples=["Car", "Truck", "Motorcycle", 'Bus', 'Van'], description="The type of the vehicle.")
+    License: str = Field(..., description="The license plate number of the vehicle.")
+    Make: str = Field(..., examples=["Toyota", "Honda", "Ford", "Suzuki"], description="The Make of the vehicle.")
+    Model: str = Field(..., examples=["Corolla", "Civic", "F-150"], description="The Model of the vehicle.")
+    Color: str = Field(..., example=["Red", "Blue", "Black", "White"], description="Return the color of the vehicle.")
+    Year: str = Field(None, description="The year of the vehicle.")
+    Condition: str = Field(None, description="The condition of the vehicle.")
+    Logo: str = Field(None, description="The visible logo of the vehicle, if applicable.")
+    Damage: str = Field(None, description="Any visible damage or wear and tear on the vehicle.")
+    Region: str = Field(None, description="Region or country based on the license plate or clues from the image.")
+    PlateType: str = Field(None, description="Type of license plate, e.g., government, personal.")
+
+# Parser for vehicle details
+parser = JsonOutputParser(pydantic_object=Vehicle)
+instructions = parser.get_format_instructions()
+
+# Image encoding function (for base64 encoding)
+def image_encoding(inputs):
+    """Load and convert image to base64 encoding"""
+    with open(inputs["image_path"], "rb") as image_file:
+        image_base64 = base64.b64encode(image_file.read()).decode("utf-8")
+    return {"image": image_base64}
+
+# Image display in grid (for multiple images)
+def display_image_grid(image_paths, rows=2, cols=3, figsize=(10, 7)):
+    fig = plt.figure(figsize=figsize)
+    max_images = rows * cols
+    image_paths = image_paths[:max_images]
+
+    for idx, path in enumerate(image_paths):
+        ax = fig.add_subplot(rows, cols, idx + 1)
+        img = mpimg.imread(path)
+        ax.imshow(img)
+        ax.axis('off')
+        filename = path.split('/')[-1]
+        ax.set_title(filename)
+
+    plt.tight_layout()
+    st.pyplot(fig)
+
+# Create the prompt for the AI model
+@chain
+def prompt(inputs):
+    prompt = [
+        SystemMessage(content="""You are an AI assistant tasked with extracting detailed information from a vehicle image. Please extract the following details:
+        - Vehicle type (e.g., Car, Truck, Bus)
+        - License plate number and type (if identifiable, such as personal, commercial, government)
+        - Vehicle make, model, and year (e.g., 2020 Toyota Corolla)
+        - Vehicle color and condition (e.g., Red, well-maintained, damaged)
+        - Any visible brand logos or distinguishing marks (e.g., Tesla logo)
+        - Details of any visible damage (e.g., scratches, dents)
+        - Vehicle’s region or country (based on the license plate or other clues)
+        If some details are unclear or not visible, return None for those fields. Do not guess or provide inaccurate information."""
+        ),
+        HumanMessage(
+            content=[
+                {"type": "text", "text": "Analyze the vehicle in the image and extract as many details as possible, including type, license plate, make, model, year, condition, damage, etc."},
+                {"type": "text", "text": instructions},  # include any other format instructions here
+                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{inputs['image']}", "detail": "low"}}
+            ]
+        )
+    ]
+    return prompt
+
+# Invoke the model for extracting vehicle details
+@chain
+def MLLM_response(inputs):
+    model: ChatOpenAI = ChatOpenAI(model="gpt-4o-2024-08-06", temperature=0.0, max_tokens=1024)
+    output = model.invoke(inputs)
+    return output.content
+
+# The complete pipeline for extracting vehicle details
+pipeline = image_encoding | prompt | MLLM_response | parser
+
+# Streamlit Interface for uploading images and showing results
+st.header("Upload a Vehicle Image for Information Extraction")
+
+uploaded_image = st.file_uploader("Choose a JPEG image", type="jpeg")
+
+if uploaded_image is not None:
+    # Display the uploaded image
+    image = PILImage.open(uploaded_image)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Convert the uploaded image to base64
+    image_path = "/tmp/uploaded_image.jpeg"
+    with open(image_path, "wb") as f:
+        f.write(uploaded_image.getbuffer())
+
+    # Process the image through the pipeline
+    output = pipeline.invoke({"image_path": image_path})
+
+    # Show the results in a user-friendly format
+    st.subheader("Extracted Vehicle Information")
+    st.json(output)
+
+    # Optionally, display more vehicle images from the folder
+    img_dir = "/content/images"
+    image_paths = glob.glob(os.path.join(img_dir, "*.jpeg"))
+    display_image_grid(image_paths)
+
+# You can also allow users to upload and process a batch of images
+st.sidebar.header("Batch Image Upload")
+
+batch_images = st.sidebar.file_uploader("Upload Images", type="jpeg", accept_multiple_files=True)
+
+if batch_images:
+    batch_input = [{"image_path": f"/tmp/{file.name}"} for file in batch_images]
+    for file in batch_images:
+        with open(f"/tmp/{file.name}", "wb") as f:
+            f.write(file.getbuffer())
+    
+    # Process the batch and display the results in a DataFrame
+    batch_output = pipeline.batch(batch_input)
+    df = pd.DataFrame(batch_output)
+    st.dataframe(df)
+
+    # Show images in a grid
+    image_paths = [f"/tmp/{file.name}" for file in batch_images]
+    display_image_grid(image_paths)