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ketanmalempati · May 7, 2024 · ba43bae · ba43bae
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commit ba43bae
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diff --git a/app.py b/app.py
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+
+
+import streamlit as st
+st.set_option('deprecation.showfileUploaderEncoding', False)
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
+from PIL import Image
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+import torch
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from together import Together
+
+@st.cache(allow_output_mutation=True)  # Cache models
+def load_models():
+    # Model initialization
+    processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
+    model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", torch_dtype=torch.float16) 
+    model.to("cuda:0")
+
+    model_name = "aaditya/OpenBioLLM-Llama3-8B-GGUF"
+    model_file = "openbiollm-llama3-8b.Q5_K_M.gguf"
+    model_path = hf_hub_download(model_name, filename=model_file, local_dir='/content')
+    llm = Llama(model_path=model_path.lstrip(), n_gpu_layers=-1)
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+    vectordb_diabetes = Chroma(persist_directory="/content/drive/MyDrive/266/diabetes/data", embedding_function=embeddings)
+    vectordb_asthma = Chroma(persist_directory="/content/drive/MyDrive/266/asthma/data", embedding_function=embeddings)
+    vectordb_dialysis = Chroma(persist_directory="/content/drive/MyDrive/266/dialysis/data", embedding_function=embeddings)
+
+    return processor, model, llm, embeddings, vectordb_diabetes, vectordb_asthma, vectordb_dialysis
+
+# Load models
+processor, model, llm, embeddings, vectordb_diabetes, vectordb_asthma, vectordb_dialysis = load_models()
+
+def getcontext_diabetes(query):
+  docs = vectordb_diabetes.similarity_search(query,k=5)
+  return docs
+
+def greet_diabetes(question):
+  client = Together(api_key="f96d39397eef84c3fc286f3ce491f429dbd77232adfb79fc893b5a0bce9c819b")
+  context =getcontext_diabetes(question)[0].page_content
+  response = client.chat.completions.create(
+      model="meta-llama/Llama-3-8b-chat-hf",
+      messages=[{"role": "user","content": "[INST] Answer the question in detail based on the following context: "+context+ " Question: "+question+"[/INST]"}],
+  )
+  # return(response.choices[0].message.content)
+  print(response.choices[0].message.content)
+  st.text_area("Output", response.choices[0].message.content, height=300)
+
+def getcontext_asthma(query):
+  docs = vectordb_asthma.similarity_search(query,k=5)
+  return docs
+
+def greet_asthma(question):
+  client = Together(api_key="f96d39397eef84c3fc286f3ce491f429dbd77232adfb79fc893b5a0bce9c819b")
+  context =getcontext_asthma(question)[0].page_content
+  response = client.chat.completions.create(
+      model="meta-llama/Llama-3-8b-chat-hf",
+      messages=[{"role": "user","content": "[INST] Answer the question in detail based on the following context: "+context+ " Question: "+question+"[/INST]"}],
+  )
+  # return(response.choices[0].message.content)
+  print(response.choices[0].message.content)
+  st.text_area("Output", response.choices[0].message.content, height=300)
+
+
+def getcontext_dialysis(query):
+  docs = vectordb_dialysis.similarity_search(query,k=5)
+  return docs
+
+def greet_dialysis(question):
+  client = Together(api_key="f96d39397eef84c3fc286f3ce491f429dbd77232adfb79fc893b5a0bce9c819b")
+  context =getcontext_dialysis(question)[0].page_content
+  response = client.chat.completions.create(
+      model="meta-llama/Llama-3-8b-chat-hf",
+      messages=[{"role": "user","content": "[INST] Answer the question in detail based on the following context: "+context+ " Question: "+question+"[/INST]"}],
+  )
+  # return(response.choices[0].message.content)
+  print(response.choices[0].message.content)
+  st.text_area("Output", response.choices[0].message.content, height=300)
+
+# Function definitions
+def answer_question(image_url):
+    # Function to answer question from image
+    image = Image.open(image_url)
+    prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"
+    inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
+    output = model.generate(**inputs, max_new_tokens=1000)
+    print(processor.decode(output[0], skip_special_tokens=True))
+    return processor.decode(output[0], skip_special_tokens=True)
+
+def process_response(prompt, response):
+    # Function to process response
+    Question = response
+    prompt = f"{prompt} Medical Question: {Question} Medical Answer:"
+    response = llm(prompt, max_tokens=10000)['choices'][0]['text']
+    return response
+
+def process_response_question(prompt):
+    # Function to process response to question
+    Question = prompt
+    prompt = f"You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs with Open Life Science AI. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience. Medical Question: {Question} Medical Answer:"
+    response = llm(prompt, max_tokens=10000)['choices'][0]['text']
+    print(response)
+    st.text_area("Output", response, height=300)
+
+
+def process_image_and_question(final_prompt, image):
+    # Function to process image and question
+    if image is not None:
+        response = answer_question(image)
+        final_response = process_response(final_prompt, response)
+        print(final_response)
+        st.text_area("Output", final_response, height=300)
+
+
+
+def update_visibility(selected_option):
+    # Function to update visibility based on dropdown selection
+    if selected_option == "General Question Doctor":
+        q = st.text_input("Enter Question")
+        if st.button("Run"):
+            process_response_question(q)
+    elif selected_option == "Prescription Questions":
+        image = st.file_uploader("Upload Image", type=["jpg", "png"])
+        final_prompt = st.text_area("Enter Text")
+        if st.button("Run"):
+            process_image_and_question(final_prompt, image)
+
+    elif selected_option == "Diabetes Doctor":
+        q = st.text_input("Enter Question")
+        if st.button("Run"):
+            greet_diabetes(q)
+
+    elif selected_option == "Asthma Doctor":
+        q = st.text_input("Enter Question")
+        if st.button("Run"):
+            greet_asthma(q)
+
+    elif selected_option == "Dialysis Doctor":
+      q = st.text_input("Enter Question")
+      if st.button("Run"):
+          greet_dialysis(q)
+
+    else:
+        st.empty()
+
+# Streamlit UI
+option_dropdown = st.selectbox("Select Option", ["Select Option", "General Question Doctor", "Prescription Questions", "Diabetes Doctor", "Asthma Doctor", "Dialysis Doctor"])
+update_visibility(option_dropdown)