Update Coreectcodewithoutfronted.py

Coreectcodewithoutfronted.py

@@ -1,141 +1,141 @@
-import os
-import gc
-import torch
-from transformers import LlamaTokenizer, LlamaForCausalLM, StoppingCriteria, StoppingCriteriaList
-
-# =============================
-# Configuration
-# =============================
-MODEL_PATH = r"C:\Users\JAY\Downloads\Chatdoc\ChatDoctor\pretrained"
-MAX_NEW_TOKENS = 200
-TEMPERATURE = 0.5
-TOP_K = 50
-REPETITION_PENALTY = 1.1
-
-# Detect device
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print(f"Loading model from {MODEL_PATH} on {device}...")
-
-# =============================
-# Load Tokenizer and Model
-# =============================
-tokenizer = LlamaTokenizer.from_pretrained(MODEL_PATH)
-model = LlamaForCausalLM.from_pretrained(
-    MODEL_PATH,
-    device_map="auto",
-    torch_dtype=torch.float16,
-    low_cpu_mem_usage=True
-)
-
-generator = model.generate
-print("✅ ChatDoctor model loaded successfully!\n")
-
-# =============================
-# Stopping Criteria
-# =============================
-class StopOnTokens(StoppingCriteria):
-    def __init__(self, stop_ids):
-        self.stop_ids = stop_ids
-
-    def __call__(self, input_ids, scores, **kwargs):
-        for stop_id_seq in self.stop_ids:
-            if len(stop_id_seq) == 1:
-                if input_ids[0][-1] == stop_id_seq[0]:
-                    return True
-            else:
-                if len(input_ids[0]) >= len(stop_id_seq):
-                    if input_ids[0][-len(stop_id_seq):].tolist() == stop_id_seq:
-                        return True
-        return False
-
-# =============================
-# Chat History
-# =============================
-history = ["ChatDoctor: I am ChatDoctor, your AI medical assistant. How can I help you today?"]
-
-# =============================
-# Get Response Function
-# =============================
-def get_response(user_input):
-    global history
-    human_invitation = "Patient: "
-    doctor_invitation = "ChatDoctor: "
-
-    # Add user input to history
-    history.append(human_invitation + user_input)
-
-    # Build conversation prompt
-    prompt = "\n".join(history) + "\n" + doctor_invitation
-    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
-
-    # Define stop words and their token IDs
-    stop_words = ["Patient:", "\nPatient:", "Patient :", "\n\nPatient"]
-    stop_ids = [tokenizer.encode(word, add_special_tokens=False) for word in stop_words]
-    stopping_criteria = StoppingCriteriaList([StopOnTokens(stop_ids)])
-
-    # Generate model response
-    with torch.no_grad():
-        output_ids = generator(
-            input_ids,
-            max_new_tokens=MAX_NEW_TOKENS,
-            do_sample=True,
-            temperature=TEMPERATURE,
-            top_k=TOP_K,
-            repetition_penalty=REPETITION_PENALTY,
-            stopping_criteria=stopping_criteria,
-            pad_token_id=tokenizer.eos_token_id,
-            eos_token_id=tokenizer.eos_token_id
-        )
-
-    # Decode and clean response
-    full_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    response = full_output[len(prompt):].strip()
-    
-    # Remove any "Patient:" that might have slipped through
-    for stop_word in ["Patient:", "Patient :", "\nPatient:", "\nPatient", "Patient"]:
-        if stop_word in response:
-            response = response.split(stop_word)[0].strip()
-            break
-
-    # Remove any leading/trailing punctuation artifacts
-    response = response.strip()
-
-    history.append(doctor_invitation + response)
-
-    # Free memory
-    del input_ids, output_ids
-    gc.collect()
-    torch.cuda.empty_cache()
-
-    return response
-
-# =============================
-# Chat Loop
-# =============================
-if __name__ == "__main__":
-    print("\n=== ChatDoctor is ready! ===")
-    print("You (the human) = Patient ")
-    print("AI = ChatDoctor")
-    print("Type 'exit' or 'quit' to end the chat.\n")
-
-    print("ChatDoctor: Hi there! How can I help you today?\n")
-
-    while True:
-        try:
-            user_input = input("Patient: ").strip()
-            if user_input.lower() in ["exit", "quit"]:
-                print("ChatDoctor: Take care! Goodbye ")
-                break
-
-            if not user_input:
-                continue
-
-            response = get_response(user_input)
-            print("ChatDoctor:", response, "\n")
-
-        except KeyboardInterrupt:
-            print("\nChatDoctor: Take care! Goodbye")
-            break
-        except Exception as e:
-            print(f"Error: {e}")
+import os
+import gc
+import torch
+from transformers import LlamaTokenizer, LlamaForCausalLM, StoppingCriteria, StoppingCriteriaList
+
+# =============================
+# Configuration
+# =============================
+MODEL_PATH = r"zl111/ChatDoctor"
+MAX_NEW_TOKENS = 200
+TEMPERATURE = 0.5
+TOP_K = 50
+REPETITION_PENALTY = 1.1
+
+# Detect device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Loading model from {MODEL_PATH} on {device}...")
+
+# =============================
+# Load Tokenizer and Model
+# =============================
+tokenizer = LlamaTokenizer.from_pretrained(MODEL_PATH)
+model = LlamaForCausalLM.from_pretrained(
+    MODEL_PATH,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True
+)
+
+generator = model.generate
+print("✅ ChatDoctor model loaded successfully!\n")
+
+# =============================
+# Stopping Criteria
+# =============================
+class StopOnTokens(StoppingCriteria):
+    def __init__(self, stop_ids):
+        self.stop_ids = stop_ids
+
+    def __call__(self, input_ids, scores, **kwargs):
+        for stop_id_seq in self.stop_ids:
+            if len(stop_id_seq) == 1:
+                if input_ids[0][-1] == stop_id_seq[0]:
+                    return True
+            else:
+                if len(input_ids[0]) >= len(stop_id_seq):
+                    if input_ids[0][-len(stop_id_seq):].tolist() == stop_id_seq:
+                        return True
+        return False
+
+# =============================
+# Chat History
+# =============================
+history = ["ChatDoctor: I am ChatDoctor, your AI medical assistant. How can I help you today?"]
+
+# =============================
+# Get Response Function
+# =============================
+def get_response(user_input):
+    global history
+    human_invitation = "Patient: "
+    doctor_invitation = "ChatDoctor: "
+
+    # Add user input to history
+    history.append(human_invitation + user_input)
+
+    # Build conversation prompt
+    prompt = "\n".join(history) + "\n" + doctor_invitation
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+
+    # Define stop words and their token IDs
+    stop_words = ["Patient:", "\nPatient:", "Patient :", "\n\nPatient"]
+    stop_ids = [tokenizer.encode(word, add_special_tokens=False) for word in stop_words]
+    stopping_criteria = StoppingCriteriaList([StopOnTokens(stop_ids)])
+
+    # Generate model response
+    with torch.no_grad():
+        output_ids = generator(
+            input_ids,
+            max_new_tokens=MAX_NEW_TOKENS,
+            do_sample=True,
+            temperature=TEMPERATURE,
+            top_k=TOP_K,
+            repetition_penalty=REPETITION_PENALTY,
+            stopping_criteria=stopping_criteria,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id
+        )
+
+    # Decode and clean response
+    full_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    response = full_output[len(prompt):].strip()
+    
+    # Remove any "Patient:" that might have slipped through
+    for stop_word in ["Patient:", "Patient :", "\nPatient:", "\nPatient", "Patient"]:
+        if stop_word in response:
+            response = response.split(stop_word)[0].strip()
+            break
+
+    # Remove any leading/trailing punctuation artifacts
+    response = response.strip()
+
+    history.append(doctor_invitation + response)
+
+    # Free memory
+    del input_ids, output_ids
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    return response
+
+# =============================
+# Chat Loop
+# =============================
+if __name__ == "__main__":
+    print("\n=== ChatDoctor is ready! ===")
+    print("You (the human) = Patient ")
+    print("AI = ChatDoctor")
+    print("Type 'exit' or 'quit' to end the chat.\n")
+
+    print("ChatDoctor: Hi there! How can I help you today?\n")
+
+    while True:
+        try:
+            user_input = input("Patient: ").strip()
+            if user_input.lower() in ["exit", "quit"]:
+                print("ChatDoctor: Take care! Goodbye ")
+                break
+
+            if not user_input:
+                continue
+
+            response = get_response(user_input)
+            print("ChatDoctor:", response, "\n")
+
+        except KeyboardInterrupt:
+            print("\nChatDoctor: Take care! Goodbye")
+            break
+        except Exception as e:
+            print(f"Error: {e}")
             print("Please try again.\n")