Upload app.py

app.py

@@ -1,25 +1,43 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 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)
+# GPT-4 credentials
+PAT_GPT4 = "3ca5bd8b0f2244eb8d0e4b2838fc3cf1"
+USER_ID_GPT4 = "openai"
+APP_ID_GPT4 = "chat-completion"
+MODEL_ID_GPT4 = "openai-gpt-4-vision"
+MODEL_VERSION_ID_GPT4 = "266df29bc09843e0aee9b7bf723c03c2"
+
+# DALL-E credentials
+PAT_DALLE = "bfdeb4029ef54d23a2e608b0aa4c00e4"
+USER_ID_DALLE = "openai"
+APP_ID_DALLE = "dall-e"
+MODEL_ID_DALLE = "dall-e-3"
+MODEL_VERSION_ID_DALLE = "dc9dcb6ee67543cebc0b9a025861b868"
+
+# TTS credentials
+PAT_TTS = "bfdeb4029ef54d23a2e608b0aa4c00e4"
+USER_ID_TTS = "openai"
+APP_ID_TTS = "tts"
+MODEL_ID_TTS = "openai-tts-1"
+MODEL_VERSION_ID_TTS = "fff6ce1fd487457da95b79241ac6f02d"
+
+# NewsGuardian model credentials
+PAT_NEWSGUARDIAN = "your_news_guardian_pat"
+USER_ID_NEWSGUARDIAN = "your_user_id"
+APP_ID_NEWSGUARDIAN = "your_app_id"
+MODEL_ID_NEWSGUARDIAN = "your_model_id"
+MODEL_VERSION_ID_NEWSGUARDIAN = "your_model_version_id"
 
 # 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,)
+userDataObject_tts = resources_pb2.UserAppIDSet(user_id=USER_ID_TTS, app_id=APP_ID_TTS)
 
 # Streamlit app
 st.title("NewsGuardian")
@@ -27,62 +45,76 @@ 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
+# Function to generate text using the "microsoft/phi-2" model
+def generate_phi2_text(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt", return_attention_mask=False)
+    outputs = model.generate(**inputs, max_length=200)
+    generated_text = tokenizer.batch_decode(outputs)[0]
+    return generated_text
+
+# User input
+raw_text_phi2 = st.text_area("Enter text for phi-2 model")
+
+# Button to generate result using "microsoft/phi-2" model
+if st.button("NewsGuardian model Generated fake news with phi-2"):
+    if raw_text_phi2:
+        generated_text_phi2 = generate_phi2_text(raw_text_phi2)
+        st.text("NewsGuardian model Generated fake news with phi-2")
+        st.text(generated_text_phi2)
+    else:
+        st.warning("Please enter news phi-2 model")
 
 # 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"])
+raw_text_news_guardian = st.text_area("This news is real or fake?")
+image_upload_news_guardian = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
 
-# Button to generate result
+# Button to generate result for NewsGuardian model
 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)
+        channel_news_guardian = ClarifaiChannel.get_grpc_channel()
+        stub_news_guardian = service_pb2_grpc.V2Stub(channel_news_guardian)
+        metadata_news_guardian = (('authorization', 'Key ' + PAT_NEWSGUARDIAN),)
+        userDataObject_news_guardian = resources_pb2.UserAppIDSet(user_id=USER_ID_NEWSGUARDIAN, app_id=APP_ID_NEWSGUARDIAN)
 
         # Prepare the request for NewsGuardian model
-        input_data_gpt4 = resources_pb2.Data()
+        input_data_news_guardian = resources_pb2.Data()
 
-        if raw_text:
-            input_data_gpt4.text.raw = raw_text
+        if raw_text_news_guardian:
+            input_data_news_guardian.text.raw = raw_text_news_guardian
 
-        if image_upload is not None:
-            image_bytes_gpt4 = image_upload.read()
-            input_data_gpt4.image.base64 = image_bytes_gpt4
+        if image_upload_news_guardian is not None:
+            image_bytes_news_guardian = image_upload_news_guardian.read()
+            input_data_news_guardian.image.base64 = image_bytes_news_guardian
 
-        post_model_outputs_response_gpt4 = stub_gpt4.PostModelOutputs(
+        post_model_outputs_response_news_guardian = stub_news_guardian.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)]
+                user_app_id=userDataObject_news_guardian,
+                model_id=MODEL_ID_NEWSGUARDIAN,
+                version_id=MODEL_VERSION_ID_NEWSGUARDIAN,
+                inputs=[resources_pb2.Input(data=input_data_news_guardian)]
             ),
-            metadata=metadata_gpt4  # Use metadata directly in the gRPC request
+            metadata=metadata_news_guardian  # 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}")
+        if post_model_outputs_response_news_guardian.status.code != status_code_pb2.SUCCESS:
+            st.error(f"NewsGuardian model API request failed: {post_model_outputs_response_news_guardian.status.description}")
         else:
             # Get the output for NewsGuardian model
-            output_gpt4 = post_model_outputs_response_gpt4.outputs[0].data
+            output_news_guardian = post_model_outputs_response_news_guardian.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"):
+            if output_news_guardian.HasField("image"):
+                st.image(output_news_guardian.image.base64, caption='Generated Image (NewsGuardian model)', use_column_width=True)
+            elif output_news_guardian.HasField("text"):
                 # Display the text result
-                st.text(output_gpt4.text.raw)
+                st.text(output_news_guardian.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_input_data.text.raw = output_news_guardian.text.raw
 
                 tts_response = stub_tts.PostModelOutputs(
                     service_pb2.PostModelOutputsRequest(
@@ -99,7 +131,7 @@ if st.button("NewsGuardian News Result"):
                     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}")
+                    st.error(f"TTS API request failed: {tts_response.status.description}")
 
     elif model_type == "DALL-E":
         # Set up gRPC channel for DALL-E
@@ -111,8 +143,8 @@ if st.button("NewsGuardian News Result"):
         # Prepare the request for DALL-E
         input_data_dalle = resources_pb2.Data()
 
-        if raw_text:
-            input_data_dalle.text.raw = raw_text
+        if raw_text_news_guardian:
+            input_data_dalle.text.raw = raw_text_news_guardian
 
         post_model_outputs_response_dalle = stub_dalle.PostModelOutputs(
             service_pb2.PostModelOutputsRequest(