Fixed divergent branch

app.py

@@ -7,6 +7,7 @@ import torch
 import faiss
 import numpy as np
 import json
+<<<<<<< Updated upstream
 
 def clean_payload(payload):
     # Remove "data:" prefix and clean newline characters
@@ -18,11 +19,18 @@ def clean_payload(payload):
         json_payload = None
     return json_payload
 
+=======
+from huggingface_hub import InferenceClient  # Keeping Hugging Face Client as requested
+
+def clean_payload(payload):
+    cleaned_payload = payload.lstrip("data:").rstrip("\n")
+    return json.loads(cleaned_payload)
+>>>>>>> Stashed changes
 
 # API Keys and Org ID
 openai.api_key = os.getenv("OPENAI_API_KEY")
 openai.organization = os.getenv("OPENAI_ORG_ID")
-serper_api_key = os.getenv("SERPER_API_KEY")
+serper_api_key = os.getenv("SERPER_API_KEY")  # SERPER API key from environment variable
 
 # Load PubMedBERT tokenizer and model
 tokenizer = BertTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract")
@@ -32,37 +40,29 @@ model = BertForSequenceClassification.from_pretrained("microsoft/BiomedNLP-PubMe
 dimension = 768
 index = faiss.IndexFlatL2(dimension)
 
-# Function to embed text (PubMedBERT)
+# Embed text (PubMedBERT)
 def embed_text(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
     outputs = model(**inputs, output_hidden_states=True)
-    hidden_state = outputs.hidden_states[-1]
-    return hidden_state.mean(dim=1).detach().numpy()
+    return outputs.hidden_states[-1].mean(dim=1).detach().numpy()
 
-# Function to retrieve info from PubMedBERT
+# Handle FDA query
 def handle_fda_query(query):
     inputs = tokenizer(query, return_tensors="pt", padding="max_length", truncation=True, max_length=512)
-    outputs = model(**inputs)
-    logits = outputs.logits
-    prediction = torch.argmax(logits, dim=1).item()
-
-    # Simulate a meaningful FDA-related response
-    if prediction == 1:
-        return f"FDA Query Processed: '{query}' contains important regulatory information."
-    else:
-        return f"FDA Query Processed: '{query}' seems to be general and not regulatory-heavy."
+    logits = model(**inputs).logits
+    return "FDA Query Processed: Contains regulatory info." if torch.argmax(logits, dim=1).item() == 1 else "FDA Query Processed: General."
 
-# Function to enhance info via GPT-4o-mini
+# Enhance via GPT-4o-mini
 def enhance_with_gpt4o(fda_response):
-    try:
-        response = openai.ChatCompletion.create(
-            model="gpt-4o-mini",  # Correct model
-            messages=[{"role": "system", "content": "You are an expert FDA assistant."}, {"role": "user", "content": f"Enhance this FDA info: {fda_response}"}],
-            max_tokens=150
-        )
-        return response['choices'][0]['message']['content']
-    except Exception as e:
-        return f"Error: {str(e)}"
+    response = openai.ChatCompletion.create(
+        model="gpt-4o-mini",
+        messages=[{"role": "system", "content": "You are an expert FDA assistant."}, {"role": "user", "content": f"Enhance this FDA info: {fda_response}"}],
+        max_tokens=150
+    )
+    return response['choices'][0]['message']['content']
+
+# Initialize Hugging Face Inference Client
+client = InferenceClient("gpt-4o-mini")  # Correct model, make sure to replace with your actual model
 
 def respond(message, system_message, max_tokens, temperature, top_p):
     try:
@@ -71,9 +71,15 @@ def respond(message, system_message, max_tokens, temperature, top_p):
         
         # Stream the enhanced response via GPT-4o-mini using the client
         enhanced_response = ""
+<<<<<<< Updated upstream
         for chat_message in client.chat_completion(...):
             payload = json.loads(chat_message.lstrip("data:").rstrip("\n"))
             enhanced_response += payload["content"]  # Or however the payload structure works
+=======
+        for chat_message in client.chat_completion(...):  # Add params if needed
+            payload = clean_payload(chat_message)
+            enhanced_response += payload["content"]
+>>>>>>> Stashed changes
 
         # Return both the PubMedBERT result and the enhanced version
         return f"Original Info from PubMedBERT: {fda_response}\n\nEnhanced Info via GPT-4o-mini: {enhanced_response}"