Update app.py

app.py

@@ -1,7 +1,10 @@
 import gradio as gr
-import requests
+import genai  # Assuming this is the correct library for Gemini API
 import sympy as sp
 
+# Set up the Gemini model
+model = genai.GenerativeModel("gemini-1.5-flash")
+
 # Function for algebraic simplification
 def algebra_simplification(expression):
     try:
@@ -21,74 +24,32 @@ def algebra_solve(equation):
     except Exception as e:
         return f"Error: {str(e)}"
 
-# Gemini API function calling
-def gemini_api_call(math_problem):
-    url = "https://api.gemini.ai/v1/function_call"
-    headers = {
-        "Authorization": "Bearer YOUR_API_KEY",
-        "Content-Type": "application/json",
-    }
-    
-    # Define function descriptors for Gemini's function calling system
-    functions = [
-        {
-            "name": "algebra_simplification",
-            "description": "Simplifies an algebraic expression",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "expression": {
-                        "type": "string",
-                        "description": "The algebraic expression to simplify"
-                    }
-                },
-                "required": ["expression"]
-            }
-        },
-        {
-            "name": "algebra_solve",
-            "description": "Solves an algebraic equation",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "equation": {
-                        "type": "string",
-                        "description": "The algebraic equation to solve"
-                    }
-                },
-                "required": ["equation"]
-            }
-        }
-    ]
+# Function to handle the chat with Gemini
+def process_math_problem(math_problem):
+    chat = model.start_chat(
+        history=[
+            {"role": "user", "parts": "Hello"},
+            {"role": "model", "parts": "Great to meet you. What would you like to know?"},
+        ]
+    )
     
-    # Payload for the Gemini API request
-    data = {
-        "model": "gemini-1.5-flash",
-        "prompt": math_problem,
-        "functions": functions,
-        "function_call": "auto"  # Automatically choose the function to call
-    }
-
-    response = requests.post(url, headers=headers, json=data)
+    # Send the math problem to Gemini
+    response = chat.send_message(math_problem)
     
-    if response.status_code == 200:
-        result = response.json()
-        return result['output']  # Adjust according to API response structure
-    else:
-        return f"Error: {response.status_code} - {response.text}"
+    # Return Gemini's response
+    return response.text
 
 # Gradio interface
-def process_math_problem(math_problem):
-    # Call Gemini API and return the result
-    return gemini_api_call(math_problem)
-
 with gr.Blocks() as demo:
     gr.Markdown("# Gemini Algebra Solver")
-
+    
+    # Single input field for math problems
     input_problem = gr.Textbox(label="Enter a Math Problem", placeholder="e.g., Simplify (x^2 - 4)/(x - 2) or Solve x^2 - 4 = 0")
     output_solution = gr.Textbox(label="Solution")
     
+    # Button to trigger the solution process
     solve_button = gr.Button("Solve")
     solve_button.click(fn=process_math_problem, inputs=input_problem, outputs=output_solution)
 
+# Launch the Gradio interface
 demo.launch()