Update app.py

app.py

@@ -4,9 +4,9 @@ import markdowm as md
 import gradio as gr
 import base64
 import datetime
-import os 
+import os
 
-# client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
+# Initialize the model client
 client = InferenceClient(
     model="mistralai/Mixtral-8x7B-Instruct-v0.1",
     token=os.getenv("token")
@@ -20,74 +20,84 @@ def respond(
         temperature=0.4,
         top_p=0.95,
 ):
-    # System message defining assistant behavior
     system_message = {
         "role": "system",
-        "content":  f"Act as SARATH and respond to the user's questions professionally. SARATH is a dedicated BTech final-year student actively seeking a job. Your name is SARATH."
-                    f"Here is SARATH’s background:```{data}```. Only answer questions using the information provided here, and strictly use only the links found in this data. If an answer isn’t available within this information, notify the user politely and suggest they reach out via LinkedIn for further assistance."
-                    f"Responses should be clear, professional, and strictly in English. Avoid giving random or empty responses at all times."
-
-
+        "content": (
+            f"Act as SARATH and respond to the user's questions professionally. SARATH is a dedicated BTech final-year student actively seeking a job. Your name is SARATH. "
+            f"Here is SARATH’s background:```{data}```. Only answer questions using the information provided here, and strictly use only the links found in this data. "
+            f"If an answer isn’t available within this information, notify the user politely and suggest they reach out via LinkedIn for further assistance. "
+            f"Responses should be clear, professional, and strictly in English. Avoid giving random or empty responses at all times."
+        )
     }
 
     messages = [system_message]
 
-    # Adding conversation history
+    # Add chat history
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
-            
-    # print(f"{datetime.datetime.now()}::{{'role': 'user', 'content': val[0]}}->{{'role': 'user', 'content': val[1]}}")
-    
-    # Adding the current user input
+
+    # Add current message
     messages.append({"role": "user", "content": message})
-    
     response = ""
 
-    # Streaming the response from the API
-    for message in client.chat_completion(
-            messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
+    # Streaming safe decoding
+    for message_chunk in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
     ):
-        token = message.choices[0].delta.content
+        if not hasattr(message_chunk, "choices") or not message_chunk.choices:
+            continue
+        delta = message_chunk.choices[0].delta
+        if not delta or not hasattr(delta, "content"):
+            continue
+        token = delta.get("content", "")
         response += token
         yield response
-        
+
+    if not response.strip():
+        yield "I'm sorry, I couldn't generate a response based on the current data."
+
     print(f"{datetime.datetime.now()}::{messages[-1]['content']}->{response}\n")
-    
+
+
+# Encode image to base64
 def encode_image(image_path):
     with open(image_path, "rb") as image_file:
         return base64.b64encode(image_file.read()).decode('utf-8')
 
-# Encode the images
+
+# Load and encode logos
 github_logo_encoded = encode_image("Images/github-logo.png")
 linkedin_logo_encoded = encode_image("Images/linkedin-logo.png")
 website_logo_encoded = encode_image("Images/ai-logo.png")
 
-# Gradio interface with additional sliders for control
+# Gradio interface
 with gr.Blocks(theme=gr.themes.Ocean(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as main:
     gr.Markdown(md.title)
     with gr.Tabs():
         with gr.TabItem("My2.0", visible=True, interactive=True):
-            gr.ChatInterface(respond,
-                             chatbot=gr.Chatbot(height=500),
-                             examples=["Tell me about yourself",
-                                       'Can you walk me through some of your recent projects and explain the role you played in each?',
-                                       "What specific skills do you bring to the table that would benefit our company's AI/ML initiatives?",
-                                       "How do you stay updated with the latest trends and advancements in AI and Machine Learning?",
-                                      ]
-                            )
+            gr.ChatInterface(
+                respond,
+                chatbot=gr.Chatbot(height=500),
+                examples=[
+                    "Tell me about yourself",
+                    'Can you walk me through some of your recent projects and explain the role you played in each?',
+                    "What specific skills do you bring to the table that would benefit our company's AI/ML initiatives?",
+                    "How do you stay updated with the latest trends and advancements in AI and Machine Learning?",
+                ]
+            )
             gr.Markdown(md.description)
 
         with gr.TabItem("Resume", visible=True, interactive=True):
             gr.Markdown(data)
-    
+
     gr.HTML(md.footer.format(github_logo_encoded, linkedin_logo_encoded, website_logo_encoded))
 
 if __name__ == "__main__":
-    main.launch(share=True)
+    main.launch(share=True)