Create app.py

app.py

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+import gradio as gr
+import requests
+from datasets import load_dataset
+from transformers import pipeline
+
+# Load the dataset
+dataset = load_dataset("viber1/indian-law-dataset")['train']
+
+# Load a pre-trained language model for question-answering
+qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
+
+def get_answer_from_api(query):
+    # Use CourtListener API to get legal information
+    base_url = "https://www.courtlistener.com/api/rest/v4/search/"
+    headers = {
+        "Authorization": "Token 9c70738ed9eb3cce4f3782a91c7c8a218c180b89"  # Replace with your actual API token
+    }
+    params = {
+        "q": query,
+        "page_size": 1  # Limit the number of results returned
+    }
+
+    try:
+        response = requests.get(base_url, headers=headers, params=params)
+        response.raise_for_status()  # Raise an error for bad responses
+        results = response.json()
+
+        # Check if there are any results
+        if results.get('count', 0) > 0:
+            return results['results'][0]['case_name']  # Adjust based on actual response structure
+        else:
+            return None  # No results found
+    except requests.RequestException as e:
+        print(f"API request failed: {e}")  # Print the error message for debugging
+        return None  # Return None if there was an error
+
+def get_answer_from_dataset(query):
+    # Look for an answer in the dataset
+    for entry in dataset:
+        if query.lower() in entry['Instruction'].lower():
+            return entry['Response']
+    return None  # No answer found in the dataset
+
+def get_answer_from_model(query):
+    # Use the pre-trained model to generate an answer
+    context = " ".join([entry['Response'] for entry in dataset])  # Combine all responses from dataset
+    result = qa_model(question=query, context=context)
+    return result['answer'] if result['score'] > 0.2 else None  # Return answer if confidence score is high
+
+def respond(query):
+    # First, try to get the answer from the API
+    answer = get_answer_from_dataset(query)
+    if answer:
+        return answer  # Return if found in API
+    
+    # If not found, look in the dataset
+    answer = get_answer_from_model(query)
+    if answer:
+        return answer  # Return if found in dataset
+    
+    # If still no answer, use the model
+    return get_answer_from_api(query)
+
+# Gradio interface
+demo = gr.Interface(
+    fn=respond,
+    inputs="text",
+    outputs="text",
+    title="AI Legal Assistant",
+    description="Ask your legal queries regarding Indian laws"
+)
+
+if _name_ == "_main_":
+    demo.launch()