Update app.py

app.py

@@ -48,26 +48,39 @@ def respond(
             response += token
         yield response, history + [(message, response)]
 
-def score_argument(argument):
-    merits_keywords = ["compelling", "convincing", "strong", "solid"]
-    laws_keywords = ["statute", "law", "regulation", "act"]
-    precedents_keywords = ["precedent", "case", "ruling", "decision"]
-    verdict_keywords = ["guilty", "innocent", "verdict", "judgment"]
-
-    merits_score = sum([1 for word in merits_keywords if word in argument.lower()])
-    laws_score = sum([1 for word in laws_keywords if word in argument.lower()])
-    precedents_score = sum([1 for word in precedents_keywords if word in argument.lower()])
-    verdict_score = sum([1 for word in verdict_keywords if word in argument.lower()])
-    length_score = len(argument.split())
-
-    merits_value = merits_score * 2
-    laws_value = laws_score * 3
-    precedents_value = precedents_score * 4
-    verdict_value = verdict_score * 5
+def generate_case_outcome(prosecutor_response, defense_response):
+    prompt = f"Prosecutor's Argument: {prosecutor_response}\nDefense Attorney's Argument: {defense_response}\n\nEvaluate both arguments and determine who won the case. Provide reasons for your decision."
+    evaluation = ""
+    for message in client.chat_completion(
+        [{"role": "system", "content": "You are a legal expert evaluating the arguments presented by the prosecution and the defense."},
+         {"role": "user", "content": prompt}],
+        max_tokens=512,
+        stream=True,
+        temperature=0.6,
+        top_p=0.95,
+    ):
+        token = message.choices[0].delta.content
+        if token is not None:
+            evaluation += token
+    return evaluation
 
-    total_score = merits_value + laws_value + precedents_value + verdict_value + length_score
+def score_argument_from_outcome(outcome, argument):
+    # Simplified scoring based on keywords in the outcome
+    if "Prosecutor" in outcome:
+        prosecutor_score = outcome.count("Prosecutor") * 2
+        if "won" in outcome and "Prosecutor" in outcome:
+            prosecutor_score += 10
+    else:
+        prosecutor_score = 0
+    
+    if "Defense" in outcome:
+        defense_score = outcome.count("Defense") * 2
+        if "won" in outcome and "Defense" in outcome:
+            defense_score += 10
+    else:
+        defense_score = 0
 
-    return total_score
+    return prosecutor_score if "Prosecutor" in argument else defense_score
 
 def color_code(score):
     if score > 50:
@@ -165,8 +178,10 @@ def chat_between_bots(system_message1, system_message2, max_tokens, temperature,
     response1 = response1[:max_length]
     response2 = response2[:max_length]
     
-    score1 = score_argument(response1)
-    score2 = score_argument(response2)
+    outcome = generate_case_outcome(response1, response2)
+    
+    score1 = score_argument_from_outcome(outcome, "Prosecutor")
+    score2 = score_argument_from_outcome(outcome, "Defense")
     
     prosecutor_color = color_code(score1)
     defense_color = color_code(score2)
@@ -174,26 +189,8 @@ def chat_between_bots(system_message1, system_message2, max_tokens, temperature,
     prosecutor_score_color = f"<div class='score-box' style='background-color:{prosecutor_color};'>Score: {score1}</div>"
     defense_score_color = f"<div class='score-box' style='background-color:{defense_color};'>Score: {score2}</div>"
     
-    outcome = generate_case_outcome(response1, response2)
-    
     return response1, response2, history1, history2, shared_history, outcome, prosecutor_score_color, defense_score_color
 
-def generate_case_outcome(prosecutor_response, defense_response):
-    prompt = f"Prosecutor's Argument: {prosecutor_response}\nDefense Attorney's Argument: {defense_response}\n\nEvaluate both arguments and determine who won the case. Provide reasons for your decision."
-    evaluation = ""
-    for message in client.chat_completion(
-        [{"role": "system", "content": "You are a legal expert evaluating the arguments presented by the prosecution and the defense."},
-         {"role": "user", "content": prompt}],
-        max_tokens=512,
-        stream=True,
-        temperature=0.6,
-        top_p=0.95,
-    ):
-        token = message.choices[0].delta.content
-        if token is not None:
-            evaluation += token
-    return evaluation
-
 def update_pdf_gallery(pdf_files):
     return pdf_files