Upload 2 files

app.py

@@ -1,198 +1,40 @@
 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 AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer
+
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
 
 torch.set_default_device("cpu")
-model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", trust_remote_code=True).to(device)
-tokenizer = 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)
-
-# 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
+# Load the Phi 2 model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    "microsoft/phi-2",
+    trust_remote_code=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/phi-2",
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Streamlit UI
+st.title("Microsoft Phi 2 Streamlit App")
+
+# User input prompt
+prompt = st.text_area("Enter your prompt:", """Write a story about Nasa""")
+
+# Generate output based on user input
+if st.button("Generate Output"):
+    with torch.no_grad():
+        token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+        output_ids = model.generate(
+            token_ids.to(model.device),
+            max_new_tokens=512,
+            do_sample=True,
+            temperature=0.3
         )
 
-        # 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)
+    output = tokenizer.decode(output_ids[0][token_ids.size(1):])
+    st.text("Generated Output:")
+    st.write(output)

requirements.txt

@@ -7,6 +7,13 @@ opencv-python
 tesseract
 clarifai_grpc
 git+https://github.com/huggingface/transformers
+
+sentencepiece
+accelerate
+bitsandbytes
+einops
+streamlit
+
 torch