Upload app.py

app.py

@@ -2,31 +2,18 @@ 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
 
-# Set your Clarifai credentials and model details for NewsGuardian model
-PAT_GPT4 = '3ca5bd8b0f2244eb8d0e4b2838fc3cf1'
-USER_ID_GPT4 = 'openai'
-APP_ID_GPT4 = 'chat-completion'
-MODEL_ID_GPT4 = 'openai-gpt-4-vision'
-MODEL_VERSION_ID_GPT4 = '266df29bc09843e0aee9b7bf723c03c2'
+import torch
 
-# 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'
 
 
+torch.set_default_device("cpu")
 
-# Streamlit app
-# Set your Clarifai credentials for NewsGuardian model
-PAT_TTS = 'bfdeb4029ef54d23a2e608b0aa4c00e4'
-USER_ID_TTS = 'openai'
-APP_ID_TTS = 'tts'
-MODEL_ID_TTS = 'openai-tts-1'
-MODEL_VERSION_ID_TTS = 'fff6ce1fd487457da95b79241ac6f02d'
-
+# 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()
@@ -37,18 +24,16 @@ userDataObject_tts = resources_pb2.UserAppIDSet(user_id=USER_ID_TTS, app_id=APP_
 # 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","NewsGuardian model"])
+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"])
 
@@ -96,8 +81,6 @@ if st.button("NewsGuardian News Result"):
                 st.text(output_gpt4.text.raw)
 
                 # Convert text to speech and play the audio
-                stub_tts = service_pb2_grpc.V2Stub(channel_gpt4)  # Use the same channel for TTS
-
                 tts_input_data = resources_pb2.Data()
                 tts_input_data.text.raw = output_gpt4.text.raw
 
@@ -108,7 +91,7 @@ if st.button("NewsGuardian News Result"):
                         version_id=MODEL_VERSION_ID_TTS,
                         inputs=[resources_pb2.Input(data=tts_input_data)]
                     ),
-                    metadata=metadata_gpt4  # Use the same metadata for TTS
+                    metadata=metadata_tts  # Use the same metadata for TTS
                 )
 
                 # Check if the TTS request was successful
@@ -154,44 +137,6 @@ if st.button("NewsGuardian News Result"):
             elif output_dalle.HasField("text"):
                 st.text(output_dalle.text.raw)
 
-    elif model_type == "NewsGuardian model":
-        # 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)
-
-        # Prepare the request for NewsGuardian model
-        input_data_tts = resources_pb2.Data()
-
-        if raw_text:
-            input_data_tts.text.raw = raw_text
-
-        post_model_outputs_response_tts = 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=input_data_tts)]
-            ),
-            metadata=metadata_tts
-        )
-
-        # Check if the request was successful for NewsGuardian model
-        if post_model_outputs_response_tts.status.code != status_code_pb2.SUCCESS:
-            st.error(f"NewsGuardian model API request failed: {post_model_outputs_response_tts.status.description}")
-        else:
-            # Get the output for NewsGuardian model
-            output_tts = post_model_outputs_response_tts.outputs[0].data
-
-            # Display the result for NewsGuardian model
-            if output_tts.HasField("text"):
-                st.text(output_tts.text.raw)
-
-            if output_tts.HasField("audio"):
-                st.audio(output_tts.audio.base64, format='audio/wav')
-
-
 # Add the beautiful social media icon section
 st.markdown("""
   <div align="center">
@@ -215,9 +160,3 @@ st.markdown("""
   </div>
   <hr>
 """, unsafe_allow_html=True)
-
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