Update chat.py

chat.py

@@ -24,29 +24,49 @@ def home():
     return "Welcome to the Flask app! The server is running."
 
 # API configuration for AI-based question generation
-# COHERE_API_KEY = 'WjnDKknACe0zxHvczdo7q4vwF4WAXn2429hcPHIB'
 COHERE_API_KEY = os.getenv("COHERE_API_KEY", "")
-COHERE_API_URL = 'https://api.cohere.ai/v1/generate'
-
-
+# (1) UPDATED URL: v2 endpoint on api.cohere.com
+COHERE_API_URL = 'https://api.cohere.com/v2/chat'
 
 # Dictionary to store user conversations
 user_sessions = {}
 # Endpoint to explain grammar topics
 movie_bp = Blueprint("movie", __name__)
 
+def _extract_text_v2(resp_json: dict) -> str:
+    """
+    v2 /chat returns:
+    { "message": { "content": [ { "type": "text", "text": "..." } ] } }
+    """
+    msg = resp_json.get("message", {})
+    content = msg.get("content", [])
+    if isinstance(content, list) and content:
+        block = content[0]
+        if isinstance(block, dict):
+            return (block.get("text") or "").strip()
+    return ""
+
 def _cohere_generate(prompt: str, max_tokens: int = 1000, temperature: float = 0.7):
     api_key = current_app.config.get("COHERE_API_KEY") or COHERE_API_KEY
     if not api_key:
         return None, ("COHERE_API_KEY not set on the server", 500)
 
     headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
-    payload = {"model": "command-r-08-2024", "prompt": prompt, "max_tokens": max_tokens, "temperature": temperature}
+    # (2) UPDATED PAYLOAD: use messages instead of prompt
+    payload = {
+        "model": "command-r-08-2024",
+        "messages": [
+            {"role": "user", "content": prompt}
+        ],
+        "max_tokens": max_tokens,
+        "temperature": temperature
+    }
     try:
         r = requests.post(COHERE_API_URL, headers=headers, json=payload, timeout=30)
         if r.status_code != 200:
             return None, (f"Cohere API error: {r.text}", 502)
-        text = r.json().get("generations", [{}])[0].get("text", "").strip()
+        # (3) UPDATED PARSING: read message.content[0].text
+        text = _extract_text_v2(r.json())
         return text, None
     except Exception as e:
         current_app.logger.exception("Cohere request failed: %s", e)
@@ -95,16 +115,20 @@ def explain_grammar():
             'Content-Type': 'application/json'
         }
 
+        # (2) UPDATED PAYLOAD: messages array
         payload = {
             'model': 'command-r-08-2024',
-            'prompt': prompt,
-            'max_tokens': 1000  # Increased to allow better explanations
+            'messages': [
+                {'role': 'user', 'content': prompt}
+            ],
+            'max_tokens': 1000
         }
 
         response = requests.post(COHERE_API_URL, headers=headers, json=payload)
 
         if response.status_code == 200:
-            ai_response = response.json().get('generations', [{}])[0].get('text', '').strip()
+            # (3) UPDATED PARSING
+            ai_response = _extract_text_v2(response.json())
 
             # Store conversation history to maintain context
             conversation_history.append(f"User: {topic}\nAI: {ai_response}")
@@ -128,20 +152,18 @@ def suggest_grammar_questions():
         if not user_input:
             return jsonify({'error': 'Input is required'}), 400
 
-        
-
         prompt = f"""
-			You are a grammar expert. Given the user's input "{user_input}", generate **3 natural grammar-related questions** that people might ask.
+            You are a grammar expert. Given the user's input "{user_input}", generate **3 natural grammar-related questions** that people might ask.
 
-			- The user's input is a **partial or full grammar-related query**.
-			- AI must **infer the most likely grammar topic** based on the input.
-			- AI must **ensure all suggestions are strictly related to English grammar**.
-			- **If the input is incomplete, intelligently complete it** with the most likely grammar concept.
-			- Ensure all **questions are fully formed and relevant**.
+            - The user's input is a **partial or full grammar-related query**.
+            - AI must **infer the most likely grammar topic** based on the input.
+            - AI must **ensure all suggestions are strictly related to English grammar**.
+            - **If the input is incomplete, intelligently complete it** with the most likely grammar concept.
+            - Ensure all **questions are fully formed and relevant**.
 
-			**User input:** "{user_input}"
-			Provide exactly 3 well-structured, grammar-related questions:
-			"""
+            **User input:** "{user_input}"
+            Provide exactly 3 well-structured, grammar-related questions:
+            """
 
         # Call Cohere API
         headers = {
@@ -149,20 +171,24 @@ def suggest_grammar_questions():
             'Content-Type': 'application/json'
         }
 
+        # (2) UPDATED PAYLOAD: messages array
         payload = {
             'model': 'command-r-08-2024',
-            'prompt': prompt,
-            'max_tokens': 100,  # Only short responses needed
-            'temperature': 0.9,  # Some randomness for variety
-            'frequency_penalty': 0.8,  # Reduce repeated words
-            'presence_penalty': 0.8  # Encourage diverse questions
+            'messages': [
+                {'role': 'user', 'content': prompt}
+            ],
+            'max_tokens': 100,
+            'temperature': 0.9
         }
 
         response = requests.post(COHERE_API_URL, headers=headers, json=payload)
 
         if response.status_code == 200:
-            suggestions = response.json().get('generations', [{}])[0].get('text', '').strip().split("\n")
-            return jsonify({'suggestions': suggestions})
+            # (3) UPDATED PARSING
+            text = _extract_text_v2(response.json())
+            suggestions = [s for s in (text or "").split("\n") if s.strip()]
+            return jsonify({'suggestions': suggestions[:3]})
+            # keep exactly 3 if more lines present
         else:
             return jsonify({'error': 'Failed to fetch suggestions', 'details': response.text}), 500
 
@@ -171,9 +197,6 @@ def suggest_grammar_questions():
 
 
 def validate_topic(topic):
-
-
-
     validation_prompt = f"""
     You are an AI grammar expert. Your task is to determine if a given topic is related to **English grammar** or not.
 
@@ -188,27 +211,27 @@ def validate_topic(topic):
     **Your response must be exactly either "Grammar", "Not Grammar", or "ask grammar topics". No extra text.**
     """
 
-
-    
-
-
     headers = {
         'Authorization': f'Bearer {COHERE_API_KEY}',
         'Content-Type': 'application/json'
     }
 
+    # (2) UPDATED PAYLOAD: messages array
     payload = {
         'model': 'command-r-08-2024',
-        'prompt': validation_prompt,
-        'max_tokens': 5  # Minimal token usage for classification
+        'messages': [
+            {'role': 'user', 'content': validation_prompt}
+        ],
+        'max_tokens': 5
     }
 
     try:
         response = requests.post(COHERE_API_URL, json=payload, headers=headers)
-        validation_result = response.json().get('generations', [{}])[0].get('text', '').strip()
-        
-        # Ensure the response is strictly "Grammar" or "Not Grammar"
-        if validation_result not in ["Grammar", "Not Grammar"]:
+        # (3) UPDATED PARSING
+        validation_result = _extract_text_v2(response.json())
+
+        # Ensure the response is strictly "Grammar" or "Not Grammar" or "ask grammar topics"
+        if validation_result not in ["Grammar", "Not Grammar", "ask grammar topics"]:
             return "Not Grammar"  # Fallback to avoid incorrect responses
 
         return validation_result
@@ -217,9 +240,6 @@ def validate_topic(topic):
         return f"Error: {str(e)}"
 
 
-
-
-
 if __name__ == '__main__':
     # app.run(debug=True)
     app.register_blueprint(movie_bp, url_prefix='')  # expose /explain-grammar locally