Update app.py

app.py

@@ -6,10 +6,10 @@ from dotenv import load_dotenv
 # Load environment variables from the .env file
 load_dotenv()
 
-# Get the Gemini API key from the .env file
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+# Get the API key from the .env file
+API_KEY = os.getenv("GEMINI_API_KEY")
 
-if GEMINI_API_KEY is None:
+if API_KEY is None:
     st.error("API key not found! Please set the GEMINI_API_KEY in your .env file.")
     st.stop()
 
@@ -22,67 +22,65 @@ questions = [
 
 # Function to query the Gemini API
 def query_gemini_api(user_answers):
-    # Correct Gemini API endpoint
-    url = f"https://generativelanguage.googleapis.com/v1beta/models/text-bison-001:generateText?key={GEMINI_API_KEY}"
+    # Correct API URL
+    url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent?key={API_KEY}"
     
     headers = {'Content-Type': 'application/json'}
     
-    # Combine the user answers into a single input text
+    # Combine user responses into a single input
     input_text = " ".join(user_answers)
     
-    # Payload for the API
+    # Payload for the API request
     payload = {
-        "prompt": {
-            "text": f"Analyze the following mood based on these inputs: {input_text}. Provide suggestions to improve the mood."
-        },
-        "temperature": 0.7,
-        "maxOutputTokens": 256,
-        "topP": 0.8,
-        "topK": 40
+        "contents": [
+            {
+                "parts": [
+                    {"text": f"Analyze the mood based on these inputs: {input_text}. Provide recommendations."}
+                ]
+            }
+        ]
     }
 
     try:
-        # Send the POST request
+        # Send POST request
         response = requests.post(url, headers=headers, json=payload)
-
-        # Check if the response is successful
+        
         if response.status_code == 200:
             result = response.json()
             
-            # Extract the generated text from the response
-            generated_text = result.get("candidates", [{}])[0].get("output", "")
-            return generated_text
+            # Extract recommendations from the response
+            recommendations = result.get("contents", [{}])[0].get("parts", [{}])[0].get("text", "")
+            return recommendations
         else:
+            # Handle API error
             st.error(f"API Error {response.status_code}: {response.text}")
             return None
     except requests.exceptions.RequestException as e:
         st.error(f"An error occurred: {e}")
         return None
 
-# Streamlit app for collecting answers
+# Streamlit app
 def main():
     st.title("Mood Analysis and Suggestions")
-
     st.write("Answer the following 3 questions to help us understand your mood:")
 
-    # Collect responses from the user
+    # Collect user responses
     responses = []
     for i, question in enumerate(questions):
         response = st.text_input(f"{i+1}. {question}")
         if response:
             responses.append(response)
 
-    # If all 3 responses are collected, send them to Gemini for analysis
+    # Query the API if all questions are answered
     if len(responses) == len(questions):
         st.write("Processing your answers...")
 
         # Query the Gemini API
-        generated_text = query_gemini_api(responses)
+        recommendations = query_gemini_api(responses)
 
-        if generated_text:
-            # Display the generated mood analysis and recommendations
-            st.write("### Mood Analysis and Suggestions:")
-            st.write(generated_text)
+        if recommendations:
+            st.write("### Recommendations to Improve Your Mood:")
+            st.write(recommendations)
         else:
             st.warning("Could not generate mood analysis. Please try again later.")
     else: