import streamlit as st
from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2
# Set your Clarifai credentials and model details for GPT-4 Vision
PAT_GPT4 = '3ca5bd8b0f2244eb8d0e4b2838fc3cf1'
USER_ID_GPT4 = 'openai'
APP_ID_GPT4 = 'chat-completion'
MODEL_ID_GPT4 = 'openai-gpt-4-vision'
MODEL_VERSION_ID_GPT4 = '266df29bc09843e0aee9b7bf723c03c2'
# Set your Clarifai credentials and model details for DALL-E
PAT_DALLE = 'bfdeb4029ef54d23a2e608b0aa4c00e4'
USER_ID_DALLE = 'openai'
APP_ID_DALLE = 'dall-e'
MODEL_ID_DALLE = 'dall-e-3'
MODEL_VERSION_ID_DALLE = 'dc9dcb6ee67543cebc0b9a025861b868'
# Streamlit app
st.title("Smart Crop Adviser")
# Inserting logo
st.image("https://cdn.tractorkarvan.com/tr:f-webp/images/Blogs/smart-farming-in-india/Smart-Farming-Blog.jpg", width=200)
# Choose model type
model_type = st.radio("Select Model Type", ["GPT-4 Vision", "DALL-E"])
# Input text prompt from the user
raw_text = st.text_input("Enter a text prompt:", 'What time of day is it?')
# File upload for image
image_upload = st.file_uploader("Upload an image:", type=["jpg", "jpeg", "png"])
# Button to generate result
if st.button("Generate Result"):
if model_type == "GPT-4 Vision":
# Set up gRPC channel for GPT-4 Vision
channel_gpt4 = ClarifaiChannel.get_grpc_channel()
stub_gpt4 = service_pb2_grpc.V2Stub(channel_gpt4)
metadata_gpt4 = (('authorization', 'Key ' + PAT_GPT4),)
userDataObject_gpt4 = resources_pb2.UserAppIDSet(user_id=USER_ID_GPT4, app_id=APP_ID_GPT4)
# Prepare the request for GPT-4 Vision
input_data_gpt4 = resources_pb2.Data()
if raw_text:
input_data_gpt4.text.raw = raw_text
if image_upload is not None:
image_bytes_gpt4 = image_upload.read()
input_data_gpt4.image.base64 = image_bytes_gpt4
post_model_outputs_response_gpt4 = stub_gpt4.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
user_app_id=userDataObject_gpt4,
model_id=MODEL_ID_GPT4,
version_id=MODEL_VERSION_ID_GPT4,
inputs=[resources_pb2.Input(data=input_data_gpt4)]
),
metadata=metadata_gpt4
)
# Check if the request was successful for GPT-4 Vision
if post_model_outputs_response_gpt4.status.code != status_code_pb2.SUCCESS:
st.error(f"GPT-4 Vision API request failed: {post_model_outputs_response_gpt4.status.description}")
else:
# Get the output for GPT-4 Vision
output_gpt4 = post_model_outputs_response_gpt4.outputs[0].data
# Display the result for GPT-4 Vision
if output_gpt4.HasField("image"):
st.image(output_gpt4.image.base64, caption='Generated Image (GPT-4 Vision)', use_column_width=True)
elif output_gpt4.HasField("text"):
st.text(output_gpt4.text.raw)
elif model_type == "DALL-E":
# Set up gRPC channel for DALL-E
channel_dalle = ClarifaiChannel.get_grpc_channel()
stub_dalle = service_pb2_grpc.V2Stub(channel_dalle)
metadata_dalle = (('authorization', 'Key ' + PAT_DALLE),)
userDataObject_dalle = resources_pb2.UserAppIDSet(user_id=USER_ID_DALLE, app_id=APP_ID_DALLE)
# Prepare the request for DALL-E
input_data_dalle = resources_pb2.Data()
if raw_text:
input_data_dalle.text.raw = raw_text
post_model_outputs_response_dalle = stub_dalle.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
user_app_id=userDataObject_dalle,
model_id=MODEL_ID_DALLE,
version_id=MODEL_VERSION_ID_DALLE,
inputs=[resources_pb2.Input(data=input_data_dalle)]
),
metadata=metadata_dalle
)
# Check if the request was successful for DALL-E
if post_model_outputs_response_dalle.status.code != status_code_pb2.SUCCESS:
st.error(f"DALL-E API request failed: {post_model_outputs_response_dalle.status.description}")
else:
# Get the output for DALL-E
output_dalle = post_model_outputs_response_dalle.outputs[0].data
# Display the result for DALL-E
if output_dalle.HasField("image"):
st.image(output_dalle.image.base64, caption='Generated Image (DALL-E)', use_column_width=True)
elif output_dalle.HasField("text"):
st.text(output_dalle.text.raw)
# Add the beautiful social media icon section
st.markdown("""
""", unsafe_allow_html=True)
