changes to inference model

app.py

@@ -6,7 +6,9 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 import os
 import shutil
+from dotenv import load_dotenv
 
+load_dotenv() 
 
 shutil.rmtree("/root/.cache", ignore_errors=True)
 shutil.rmtree("/tmp", ignore_errors=True)
@@ -16,7 +18,7 @@ CHROMA_PATH = "chroma"
 KEY = os.getenv("token")
 
 # Hugging Face API setup
-repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
+repo_id = "Qwen/Qwen2.5-7B-Instruct"
 
 PROMPT_TEMPLATE = """
 Answer the question based on the context provided. If no relevant information is found, state so.
@@ -28,6 +30,10 @@ Question:
 {question}
 
 Answer:
+// Do not include the statement "Based on the provided context" in your answer. Start directly with the answer.
+// If the user mentions that he does not want to know about these courses, ask him what topic he wants to learn about in the answer.
+
+If you encounter a text message which is not related to the context, the bot must respond with "I don't have relevant information to answer that. Kindly ask queries related to free data science and machine learning courses on Analytics Vidhya."
 """
 
 # Initialize the local embedding model
@@ -61,8 +67,8 @@ class LLM:
                     "content": prompt
                 }
             ],
-            max_tokens=250,
-            temperature=0.2
+            max_tokens=500,
+            temperature=0.8
         )
         return str(completion.choices[0].message.content)
 
@@ -116,7 +122,7 @@ def predict(message, history):
 # Define the introductory content
 intro_content = """
 # Course Recommendation Bot
-This bot helps you find **free courses** available on [Analytics Vidhya](https://www.analyticsvidhya.com/).
+This bot helps you find **free courses related to python, data science and machine learning** available on [Analytics Vidhya](https://www.analyticsvidhya.com/).
 You can ask any questions related to these courses.
 
 For example:
@@ -129,4 +135,4 @@ with gr.Blocks() as demo:
     gr.Markdown(intro_content)  # Display introductory content
     chatbot = gr.ChatInterface(predict, type="messages")  # Chat interface
 
-demo.launch(share=True)
+demo.launch(share=False)