Update app.py

app.py

@@ -3,7 +3,7 @@ from huggingface_hub import InferenceClient
 from sentence_transformers import SentenceTransformer
 import torch
 import numpy as np 
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+client = InferenceClient("Qwen/Qwen2.5-7B-Instruct-1M")
 
 # Open the ECOsphere.txt file in read mode with UTF-8 encoding
 with open("ECOsphere.txt", "r", encoding="utf-8") as file:
@@ -11,23 +11,23 @@ with open("ECOsphere.txt", "r", encoding="utf-8") as file:
   ECOsphere_text = file.read()
 
 def respond(message, history):
-    top_results = get_top_chunks( message , chunk_embeddings, cleaned_chunks) # Complete this line
-    # Print the top results
-    print(top_results)
-    messages = [{ "role": "system", "content": f"You are a chatbot that encourage people to live more sustainably. Base your response on the following action {top_results}" }]
-    
+    top_results = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
+    print("Top results:", top_results)
+
+    messages = [
+        {"role": "system", "content": f"You are a chatbot that encourages people to live more sustainably. Base your response on: {top_results}"}
+    ]
     if history:
         messages.extend(history)
-        
     messages.append({"role": "user", "content": message})
-    
+
     response = client.chat_completion(
-        messages,
-        max_tokens = 1000,
-        temperature = 0.5
+        model="Qwen/Qwen2.5-7B-Instruct",
+        messages=messages,
+        max_tokens=200,
+        temperature=0.5
     )
-    print(response)
-    return response["choices"][0]["message"]["content"].strip()
+    return response.choices[0].message["content"].strip()
 
 chatbot = gr.ChatInterface(respond, type="messages")
 cleaned_chunks = []
@@ -35,8 +35,7 @@ def preprocess_text(text):
   # Strip extra whitespace from the beginning and the end of the text
   cleaned_text = text.strip()
 
-  # Split the cleaned_text by every newline character (\n)
-  chunks = cleaned_text.split("\n")
+  chunks = cleaned_text.split('*'))
 
   # Create an empty list to store cleaned chunks
   #cleaned_chunks = []