used to make dummy text using ollama #1334

Author: A-Akhil

NLP/dummysentence.py

@@ -1,66 +1,33 @@
-import os
-from sentence_transformers import SentenceTransformer, util
-
-MODEL_NAME = 'all-MiniLM-L6-v2'
-MODEL_FOLDER = 'model'
-
-def load_file(file_path):
-    with open(file_path, 'r', encoding='utf-8') as file:
-        return [line.strip() for line in file if line.strip()]
-
-def load_or_download_model():
-    model_path = os.path.join(MODEL_FOLDER, MODEL_NAME)
-    if os.path.exists(model_path):
-        print(f"Loading model from {model_path}")
-        return SentenceTransformer(model_path)
-    else:
-        print(f"Downloading model {MODEL_NAME}")
-        model = SentenceTransformer(MODEL_NAME)
-        os.makedirs(MODEL_FOLDER, exist_ok=True)
-        model.save(model_path)
-        print(f"Model saved to {model_path}")
-        return model
-
-def find_similar_sentences(query, file_path, top_n=5):
-    # Load the pre-trained model
-    model = load_or_download_model()
-
-    # Load and encode the sentences from the file
-    sentences = load_file(file_path)
-    sentence_embeddings = model.encode(sentences)
-
-    # Encode the query
-    query_embedding = model.encode([query])
-
-    # Calculate cosine similarities
-    cosine_scores = util.pytorch_cos_sim(query_embedding, sentence_embeddings)[0]
-
-    # Get top N results
-    top_results = sorted(zip(sentences, cosine_scores), key=lambda x: x[1], reverse=True)[:top_n]
-
-    return top_results
-
-def main():
-    print("Welcome to the Sentence Similarity Search Tool!")
-    
-    # Get user input for query
-    query = input("Enter your query: ")
+import ollama as client
+
+# Function to get response from Ollama API with system prompt
+def get_ollama_response(sentence_number):
+    system_prompt = "You are a bot and speak in one line. Keep your responses short and to the point."
+    stream = client.chat(
+        model="llama3.2",
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": f"Generate a unique sentence randomly for sentence number {sentence_number}."}
+        ],
+        stream=True
+    )
 
-    # Get user input for file path
-    file_name = input("Enter the name of your text file (without .txt extension): ")
-    file_path = f"{file_name}.txt"
-
-    try:
-        results = find_similar_sentences(query, file_path)
-
-        print(f"\nTop 5 similar sentences for query: '{query}'\n")
-        for sentence, score in results:
-            print(f"Similarity: {score:.4f}")
-            print(f"Sentence: {sentence}\n")
-    except FileNotFoundError:
-        print(f"Error: The file '{file_path}' was not found. Please check the file name and try again.")
-    except Exception as e:
-        print(f"An error occurred: {str(e)}")
-
-if __name__ == "__main__":
-    main()
+    response = ''
+    for chunk in stream:
+        response += chunk['message']['content']
+    return response.strip()  # Strip any leading/trailing spaces
+
+# Open the file in write mode
+with open("generated_sentences.txt", "w") as file:
+    # Loop to generate 100 sentences one by one
+    for i in range(100):
+        # Get the sentence using the function
+        sentence = get_ollama_response(i + 1)
+        
+        # Write the sentence to the file on a new line
+        file.write(sentence + "\n")
+        
+        # Print the sentence to the console
+        print(f"Sentence {i+1}: {sentence}")
+
+print("File 'generated_sentences.txt' created with 100 sentences, each on a new line.")