Create main.py

patentwiz/main.py

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+import nltk
+
+nltk.download("all", quiet=True)
+from datetime import datetime
+import random
+import json
+from . import preprocess_data
+from . import qaagent
+
+
+PROMPT = """
+Task: Carefully review the given patent text and extract as much physical measurements information such as length/distance, mass/weight, time, temperature, Volume, area, speed, pressure, energy, power, electric current 
+and voltage, frequency, force, acceleration, density, resistivity, magnetic field strength, and luminous intensity as much as possible. 
+We are particularly interested in physical measurements including substance that was measured, Value of the measurement, and Unit of the measurement, and measurement type mentioned in the text. 
+For each measurement, please provide the following details:
+- The substance that was measured. (substance)
+- The specific value or range that was measured. (Measured Value)
+- The unit of the measurement, if provided. (Unit)
+- The type of measurement being conducted (e.g., diameter, size, etc.) 
+Format your response in a structured JSON-like format, as follows:
+{"Content": [
+    {
+      "Measurement_substance": "substance",
+      "Measured_value": "value",
+      "Measured_unit": "unit",
+      "measurement_type": "type"
+    },
+    // ... additional measurements, if present
+  ]
+}
+If multiple measurements are present in the text, each should be listed as a separate object within the "Content" array.
+Example: If the text includes the sentence, "The resulting BaCO3 had a crystallite size of between about 20 and 40 nm", the output should be:
+{"Content": [
+    {
+      "Measurement_substance": "BaCO3",
+      "Measured_value": "between about 20 and 40",
+      "Measured_unit": "nm",
+      "measurement_type": "crystallite size"
+    }
+  ]
+}
+Try to provide as complete and accurate information as possible. Print only the formatted JSON response.
+"""
+
+
+def main():
+    """
+    Main function to:
+    - Authenticate with OpenAI
+    - Receive and parse date input from the user
+    - Extract and print year, month, day
+    - Preprocess patent data
+    - Analyze selected patents using GPT-3.5 Turbo
+    - Print results including cost and optionally output
+    """
+    print("Starting the patent analysis process...")
+    # Step 1: Input the date from the user
+    user_date_input = input("Enter a date in the format 'YYYY-MM-DD': ")
+
+    # Step 2: Parse the input date into a datetime object
+    try:
+        input_date = datetime.strptime(user_date_input, "%Y-%m-%d")
+    except ValueError:
+        print(
+            "Invalid date format. Please enter a valid date in the format 'YYYY-MM-DD'."
+        )
+        return
+
+    # Step 3: Extract date components
+    year = input_date.year
+    month = input_date.month
+    day = input_date.day
+
+    print("Year:", year)
+    print("Month:", month)
+    print("Day:", day)
+
+    # Step 4: Get random patents number from user
+    num_patents_to_analyze = int(
+        input("Enter the number of patents you want to analyze: ")
+    )
+
+    logging_choice = input("Do you want to log the results? (yes/no): ").strip().lower()
+    logging_enabled = logging_choice == "yes"
+
+    model_choice = input(
+        "Select a model for analysis: 1. gpt-3.5-turbo 2. gpt-4"
+    ).strip()
+
+    if model_choice == "1":
+        model_name = "gpt-3.5-turbo"
+    elif model_choice == "2":
+        model_name = "gpt-4"
+    else:
+        print("Invalid choice, defaulting to gpt-3.5-turbo.")
+        model_name = "gpt-3.5-turbo"
+
+    print("Processing patents...")
+    # Step 5: Parse and save patents
+    saved_patent_names = preprocess_data.parse_and_save_patents(year, month, day, False)
+
+    # Step 6: Select random patents and analyze
+    random_patents = random.sample(saved_patent_names, num_patents_to_analyze)
+
+    gpt_3_results = {}
+    total_cost_gpt3 = 0
+
+    # Step 7: Process patents with GPT-3.5 Turbo
+    for i in range(len(random_patents)):
+        cost, output = qaagent.call_QA_to_json(
+            PROMPT, year, month, day, random_patents, i, logging_enabled, model_name
+        )
+
+        total_cost_gpt3 += cost
+
+    average_cost_gpt3 = total_cost_gpt3 / num_patents_to_analyze
+
+    print("Patent analysis process completed successfully.")
+    # Step 8: Print results
+    print("\nResults for GPT-3.5 Turbo:")
+    print("Number of patents analyzed:", num_patents_to_analyze)
+    print("Total cost for analyzing all patents:", total_cost_gpt3)
+    print("Average cost per patent:", average_cost_gpt3)