Create app.py

app.py

@@ -0,0 +1,65 @@
+import requests
+import sys
+import spacy
+from io import BytesIO
+import pandas as pd
+import json
+
+# Load your custom NER model
+nlp_ner = spacy.load("D:/Databae/Saved Models/FINAL MODELS/NER/model-best")
+
+def send_results_to_api(data, result_url):
+
+    headers = {"Content-Type": "application/json"}
+    response = requests.post(result_url, json=data, headers=headers)
+    if response.status_code == 200:
+        return response.json()  # Return any response from the API if needed
+    else:
+        return {"error": f"Failed to send results to API: {response.status_code}"}
+
+def process_xlsx(params):
+    # xlsx_file = 'https://fragilestatesindex.org/wp-content/uploads/2023/06/FSI-2023-DOWNLOAD.xlsx'
+    try:
+        params = json.loads(params)
+    except json.JSONDecodeError as e:
+        return {"error": f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}"}
+
+    addresses = params.get("audio_files", [])
+    api = params.get("api", "")
+    job_id = params.get("job_id", "")
+
+    solutions=[]
+    text_id = 0
+    for adress in addresses:
+        doc = nlp_ner(adress)
+
+        # Initialize the dictionary to store the results
+        entities_dict = {}
+
+        # Extract entities and their indices
+        for idx, ent in enumerate(doc.ents):
+            if ent.label_ not in entities_dict:
+                entities_dict[ent.label_] = []
+            entities_dict[ent.label_].append({'word': ent.text, 'index': idx})
+
+        # Create the final output dictionary
+        output = {
+            'text': adress,
+            'text_id': text_id,
+            'answer': entities_dict
+        }
+        solutions.append(output)
+        text_id = text_id+1
+        
+    result_url = f"{api}/{job_id}"
+    # send_results_to_api(solutions, result_url)
+
+    return json.dumps({"solutions": solutions}, indent=4)
+
+
+import gradio as gr
+inputt = gr.Textbox(label="Parameters (JSON format) Eg. {'audio_files':['file1.mp3','file2.wav'], 'api':'https://api.example.com', 'job_id':'1001'}")
+outputs = gr.JSON()
+
+application = gr.Interface(fn=process_xlsx, inputs=inputt, outputs=outputs, title="Named Entity Recognition with API Integration")
+application.launch()