app.py

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

# 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)

# 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 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_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 for NewsGuardian model
if st.button("NewsGuardian News Result"):
    if model_type == "NewsGuardian model":
        # Set up gRPC channel for NewsGuardian model
        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_news_guardian = resources_pb2.Data()

        if raw_text_news_guardian:
            input_data_news_guardian.text.raw = raw_text_news_guardian

        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_news_guardian = stub_news_guardian.PostModelOutputs(
            service_pb2.PostModelOutputsRequest(
                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_news_guardian  # Use metadata directly in the gRPC request
        )

        # Check if the request was successful for NewsGuardian model
        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_news_guardian = post_model_outputs_response_news_guardian.outputs[0].data

            # Display the result for NewsGuardian model
            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_news_guardian.text.raw)

                # Convert text to speech and play the audio
                tts_input_data = resources_pb2.Data()
                tts_input_data.text.raw = output_news_guardian.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"TTS 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_news_guardian:
            input_data_dalle.text.raw = raw_text_news_guardian

        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)