Updated app.py w new code

app.py

@@ -1,63 +1,82 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from datasets import load_dataset
+from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments, Trainer
+import openai
+import faiss
+import numpy as np
+
+# Set up OpenAI API key for GPT-4
+openai.api_key = "your_openai_api_key"
+
+# Load PubMedBERT tokenizer and model
+tokenizer = BertTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract")
+model = BertForSequenceClassification.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract", num_labels=2)
+
+# Load the FDA dataset from Hugging Face
+dataset = load_dataset("pretzinger/cdx-cleared-approved", split="train")
+
+# Tokenize the dataset
+def tokenize_function(example):
+    return tokenizer(example["text"], padding="max_length", truncation=True)
+
+tokenized_dataset = dataset.map(tokenize_function, batched=True)
+
+# FAISS setup for vector search (embedding-based memory)
+dimension = 768  # PubMedBERT embedding size
+index = faiss.IndexFlatL2(dimension)
+
+def embed_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
+    outputs = model(**inputs)
+    return outputs.last_hidden_state.mean(dim=1).detach().numpy()
+
+# Example: Embed past conversation and store in FAISS
+past_conversation = "FDA approval for companion diagnostics requires careful documentation."
+past_embedding = embed_text(past_conversation)
+index.add(past_embedding)
+
+# Embed the incoming query and search for related memory
+def search_memory(query):
+    query_embedding = embed_text(query)
+    D, I = index.search(query_embedding, k=1)  # Retrieve most similar past conversation
+    return I
+
+# Function to handle FDA-related queries with PubMedBERT
+def handle_fda_query(query):
+    # If query requires specific FDA info, process it with PubMedBERT
+    inputs = tokenizer(query, return_tensors="pt", padding="max_length", truncation=True)
+    outputs = model(**inputs)
+    logits = outputs.logits
+    # Process logits for classification or output a meaningful response
+    response = "Processed FDA-related query via PubMedBERT"
+    return response
+
+# Function to handle general queries using GPT-4
+def handle_openai_query(prompt):
+    response = openai.Completion.create(
+        engine="gpt-4",  # Ensuring GPT-4 usage
+        prompt=prompt,
+        max_tokens=100
+    )
+    return response.choices[0].text.strip()
+
+# Main assistant function that delegates to either OpenAI or PubMedBERT
+def assistant(query):
+    # First, determine if query needs FDA-specific info
+    openai_response = handle_openai_query(f"Is this query FDA-related: {query}")
+    
+    if "FDA" in openai_response or "regulatory" in openai_response:
+        # Search past conversations/memory using FAISS
+        memory_index = search_memory(query)
+        if memory_index:
+            return f"Found relevant past memory: {past_conversation}"  # Return past context from memory
+
+        # If no memory match, proceed with PubMedBERT
+        return handle_fda_query(query)
+    
+    # General conversational handling with OpenAI (GPT-4)
+    return openai_response
+
+# Example Usage
+query = "What is required for PMA approval for companion diagnostics?"
+response = assistant(query)
+print(response)