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| 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 | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| import torch | |
| torch.set_default_device("cpu") | |
| # Load the 'microsoft/phi-2' model and tokenizer | |
| model_phi2 = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", trust_remote_code=True) | |
| tokenizer_phi2 = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True) | |
| # Set up gRPC channel for NewsGuardian model | |
| channel_tts = ClarifaiChannel.get_grpc_channel() | |
| stub_tts = service_pb2_grpc.V2Stub(channel_tts) | |
| metadata_tts = (('authorization', 'Key ' + PAT_TTS),) | |
| userDataObject_tts = resources_pb2.UserAppIDSet(user_id=USER_ID_TTS, app_id=APP_ID_TTS,) | |
| # Streamlit app | |
| st.title("NewsGuardian") | |
| # Inserting logo | |
| st.image("https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTdA-MJ_SUCRgLs1prqudpMdaX4x-x10Zqlwp7cpzXWCMM9xjBAJYWdJsDlLoHBqNpj8qs&usqp=CAU") | |
| # Function to get gRPC channel for NewsGuardian model | |
| def get_tts_channel(): | |
| channel_tts = ClarifaiChannel.get_grpc_channel() | |
| return channel_tts, channel_tts.metadata | |
| # User input | |
| model_type = st.selectbox("Select Model", ["NewsGuardian model", "DALL-E"]) | |
| raw_text = st.text_area("This news is real or fake?") | |
| image_upload = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"]) | |
| # Button to generate result | |
| if st.button("NewsGuardian News Result"): | |
| if model_type == "NewsGuardian model": | |
| # Set up gRPC channel for NewsGuardian model | |
| 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 NewsGuardian model | |
| 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 # Use metadata directly in the gRPC request | |
| ) | |
| # Check if the request was successful for NewsGuardian model | |
| if post_model_outputs_response_gpt4.status.code != status_code_pb2.SUCCESS: | |
| st.error(f"NewsGuardian model API request failed: {post_model_outputs_response_gpt4.status.description}") | |
| else: | |
| # Get the output for NewsGuardian model | |
| output_gpt4 = post_model_outputs_response_gpt4.outputs[0].data | |
| # Display the result for NewsGuardian model | |
| if output_gpt4.HasField("image"): | |
| st.image(output_gpt4.image.base64, caption='Generated Image (NewsGuardian model)', use_column_width=True) | |
| elif output_gpt4.HasField("text"): | |
| # Display the text result | |
| st.text(output_gpt4.text.raw) | |
| # Convert text to speech and play the audio | |
| tts_input_data = resources_pb2.Data() | |
| tts_input_data.text.raw = output_gpt4.text.raw | |
| tts_response = stub_tts.PostModelOutputs( | |
| service_pb2.PostModelOutputsRequest( | |
| user_app_id=userDataObject_tts, | |
| model_id=MODEL_ID_TTS, | |
| version_id=MODEL_VERSION_ID_TTS, | |
| inputs=[resources_pb2.Input(data=tts_input_data)] | |
| ), | |
| metadata=metadata_tts # Use the same metadata for TTS | |
| ) | |
| # Check if the TTS request was successful | |
| if tts_response.status.code == status_code_pb2.SUCCESS: | |
| tts_output = tts_response.outputs[0].data | |
| st.audio(tts_output.audio.base64, format='audio/wav') | |
| else: | |
| st.error(f"NewsGuardian model API request failed: {tts_response.status.description}") | |
| 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(""" | |
| <div align="center"> | |
| <a href="https://github.com/pyresearch/pyresearch" style="text-decoration:none;"> | |
| <img src="https://user-images.githubusercontent.com/34125851/226594737-c21e2dda-9cc6-42ef-b4e7-a685fea4a21d.png" width="2%" alt="" /></a> | |
| <img src="https://user-images.githubusercontent.com/34125851/226595799-160b0da3-c9e0-4562-8544-5f20460f7cc9.png" width="2%" alt="" /> | |
| <a href="https://www.linkedin.com/company/pyresearch/" style="text-decoration:none;"> | |
| <img src="https://user-images.githubusercontent.com/34125851/226596446-746ffdd0-a47e-4452-84e3-bf11ec2aa26a.png" width="2%" alt="" /></a> | |
| <img src="https://user-images.githubusercontent.com/34125851/226595799-160b0da3-c9e0-4562-8544-5f20460f7cc9.png" width="2%" alt="" /> | |
| <a href="https://twitter.com/Noorkhokhar10" style="text-decoration:none;"> | |
| <img src="https://user-images.githubusercontent.com/34125851/226599162-9b11194e-4998-440a-ba94-c8a5e1cdc676.png" width="2%" alt="" /></a> | |
| <img src="https://user-images.githubusercontent.com/34125851/226595799-160b0da3-c9e0-4562-8544-5f20460f7cc9.png" width="2%" alt="" /> | |
| <a href="https://www.youtube.com/@Pyresearch" style="text-decoration:none;"> | |
| <img src="https://user-images.githubusercontent.com/34125851/226599904-7d5cc5c0-89d2-4d1e-891e-19bee1951744.png" width="2%" alt="" /></a> | |
| <img src="https://user-images.githubusercontent.com/34125851/226595799-160b0da3-c9e0-4562-8544-5f20460f7cc9.png" width="2%" alt="" /> | |
| <a href="https://www.facebook.com/Pyresearch" style="text-decoration:none;"> | |
| <img src="https://user-images.githubusercontent.com/34125851/226600380-a87a9142-e8e0-4ec9-bf2c-dd6e9da2f05a.png" width="2%" alt="" /></a> | |
| <img src="https://user-images.githubusercontent.com/34125851/226595799-160b0da3-c9e0-4562-8544-5f20460f7cc9.png" width="2%" alt="" /> | |
| <a href="https://www.instagram.com/pyresearch/" style="text-decoration:none;"> | |
| <img src="https://user-images.githubusercontent.com/34125851/226601355-ffe0b597-9840-4e10-bbef-43d6c74b5a9e.png" width="2%" alt="" /></a> | |
| </div> | |
| <hr> | |
| """, unsafe_allow_html=True) | |