created

app.py

@@ -0,0 +1,125 @@
+import json
+from openai import OpenAI
+#from dotenv import load_dotenv
+import os
+import gradio as gr
+from gradio import ChatMessage
+import time
+
+#load_dotenv()
+
+#use openai API key
+# api_key = os.getenv("OPENAI_API_KEY") 
+# client = OpenAI(api_key=api_key)
+#model="gpt-4.1"
+
+#use gemini API key
+api_key = os.getenv('GEMINI_API_KEY')
+
+if api_key:
+    print("API Key Loaded Successfully!")
+else:
+    print("API Key Missing!")
+
+
+client = OpenAI(api_key=api_key,
+         base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
+)
+model="gemini-2.0-flash"
+
+
+system_prompt = """
+You are an AI assistant who is expert in breaking down complex problems into smaller, manageable parts.
+Your task is to assist the user with their questions about Maths.
+
+For the given user input, analyse the input and break down the problem step by step
+Atleast 5-6 steps are required to solve the problem.
+
+The steps are you get a user input, you analyse, you think, 
+you think again for several times and then return an output 
+with explanation and then finally you validate the output as well 
+before returning the final output.
+
+Rules:
+1. Follow the strict JSON output as per output schema
+2. Always perform one step at a time and wait for next input
+3. Carefully analyse the user query
+
+Output format:
+{{'step':"string", 'content':"string"}}
+
+Example:
+Input: What is 2 + 2.
+Output: {{'step':"analyse",'content':"The user is asking for the sum of 2 and 2."}}
+{{'step':"think",'content':"To find the sum, I need to add the two numbers together."}}
+{{'step':"output",'content':"2 + 2 = 4."}}
+{{'step':"validate",'content':"The answer is correct because 2 added to 2 equals 4."}}
+{{'step':"result",'content':"2+2=4 and that is calculated by adding all the numbers"}}
+
+"""
+#Create a list of messages
+#Using ChatML format with Gemini
+history=[
+    {"role": "system", "content": system_prompt}
+]
+
+def llm_response(message, history):
+    if(len(history) ==0):
+        history=[{"role": "system", "content": system_prompt}]
+    history.append({"role": "user", "content": message})
+    response = ChatMessage(content="", metadata={"title": "_Thinking_ step-by-step", "id": 0, "status": "pending"})
+    yield response
+
+    accumulated_thoughts = ""
+    start_time = time.time()
+    while True: 
+        llm_response = client.chat.completions.create(
+            model=model,
+            response_format={"type": "json_object"},
+            messages= history)
+
+        parsed_response = json.loads(llm_response.choices[0].message.content)
+        print("________________________________")
+        print(parsed_response)
+        print("________________________________")
+        time.sleep(2)
+
+        history.append({"role": "assistant", "content": parsed_response['content']})
+        if parsed_response['step'] == 'result':
+            #We print how much time it took to get the final result
+            response.metadata["status"] = "done"
+            response.metadata["duration"] = time.time() - start_time
+            yield response
+
+            #And here is the final result
+            thought = f"🤖Final Result: {parsed_response['content']}"
+            response = [
+			        response,
+			        ChatMessage(
+			            content=thought
+			        )
+			    ]
+            
+            yield response
+            break
+        else: #we have not reached final result yet
+            
+            thought = (parsed_response["step"], parsed_response["content"])            
+            accumulated_thoughts += f"**{thought[0]}**: {thought[1]}\n"            
+            response.content = accumulated_thoughts.strip()            
+            yield response
+ 
+
+
+demo = gr.ChatInterface(
+    llm_response,
+    title="Chain of Thought based LLM Chat Interface 🤔- [email protected]",
+    type="messages",
+    theme='amitguptaforwork/blue_professional',
+    examples=["how to calculate 2^2 + 5^2", "What is pythagorus theorem", "What is 2+5*6"],
+    
+)
+
+demo.launch()
+
+