Update app.py

app.py

@@ -1,33 +1,237 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
+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
 import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# Use GPU if available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-st.title("Text Generation with Hugging Face Transformers")
+# 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"
 
-# Input prompt from user
-prompt = st.text_area("Enter a prompt:", "this news is real pyresearch given right computer vision videos?")
+# 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"
 
-# Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", trust_remote_code=True)
+# TTS credentials
+PAT_TTS = "bfdeb4029ef54d23a2e608b0aa4c00e4"
+USER_ID_TTS = "openai"
+APP_ID_TTS = "tts"
+MODEL_ID_TTS = "openai-tts-1"
+MODEL_VERSION_ID_TTS = "fff6ce1fd487457da95b79241ac6f02d"
 
-# Move the model to the desired device
-model.to(device)
+# 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"
 
-# Generate text on button click
-if st.button("Generate"):
-    with torch.no_grad():
-        token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(device)
-        output_ids = model.generate(
-            token_ids,
-            max_new_tokens=512,
-            do_sample=True,
-            temperature=0.1
+#
+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 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","NewsGuardian model"])
+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
         )
 
-    generated_text = tokenizer.decode(output_ids[0][token_ids.size(1):])
-    st.text("Generated Text:")
-    st.write(generated_text)
+        # 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
+                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
+
+                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_gpt4  # 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)
+
+    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">
+      <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)
+
+
+
+
+