Create app.py

app.py

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+import streamlit as st
+import json
+from transformers import pipeline
+
+# Load pre-trained models
+sentiment_analyzer = pipeline('sentiment-analysis', model='nlptown/bert-base-multilingual-uncased-sentiment')
+emotion_analyzer = pipeline('text-classification', model='j-hartmann/emotion-english-distilroberta-base')
+intent_analyzer = pipeline('text-classification', model='Falconsai/intent_classification')
+
+# Function to determine response length based on text length
+def determine_response_length(text):
+    length = len(text.split())
+    if length < 5:
+        return "short"
+    elif length < 15:
+        return "medium"
+    else:
+        return "long"
+
+# Analyze conversation function
+def analyze_conversation(conversation):
+    output = {"conversation": []}
+    
+    for entry in conversation:
+        speaker = entry['speaker']
+        text = entry['text']
+
+        # Sentiment analysis
+        sentiment_result = sentiment_analyzer(text)
+        sentiment = sentiment_result[0]['label']
+
+        # Emotion detection
+        emotion_result = emotion_analyzer(text)
+        emotion = emotion_result[0]['label']
+
+        # Intent recognition
+        intent_result = intent_analyzer(text)
+        intent = intent_result[0]['label']
+
+        # Assign other attributes
+        tone = "formal" if speaker == "AGENT" else "informal"
+        confidence_level = "high" if speaker == "AGENT" else "low"
+        frustration_level = "low" if sentiment == "positive" else "medium"
+        response_length = determine_response_length(text)
+        action_required = "no" if speaker == "AGENT" else "yes"
+        interruption = "no"
+        cooperation_level = "high" if sentiment == "positive" else "medium"
+        clarity = "clear" if speaker == "AGENT" else "confusing"
+        objective = "inform_customer" if speaker == "AGENT" else "get_clarification"
+        timeline = "present"
+        motivation = "inform_update" if speaker == "AGENT" else "verify_information"
+        conversation_stage = "development"
+        resolution = "pending"
+        context = "sales"
+        urgency = "low" if speaker == "AGENT" else "medium"
+        problem_type = "contract_update"
+        key_words = ["contract", "electricity"]  # Consider dynamic extraction here
+        expected_detail = "basic" if speaker == "AGENT" else "detailed"
+        time_gap = "normal"
+        client_expectation = "get_information"
+        channel = "telephone"
+        power_relationship = "balanced"
+
+        # Add the entry to the conversation
+        output["conversation"].append({
+            "speaker": speaker,
+            "text": text,
+            "sentiment": sentiment,
+            "emotion": emotion,
+            "intent": intent,
+            "tone": tone,
+            "confidence_level": confidence_level,
+            "frustration_level": frustration_level,
+            "response_length": response_length,
+            "action_required": action_required,
+            "interruption": interruption,
+            "cooperation_level": cooperation_level,
+            "clarity": clarity,
+            "objective": objective,
+            "timeline": timeline,
+            "motivation": motivation,
+            "conversation_stage": conversation_stage,
+            "resolution": resolution,
+            "context": context,
+            "urgency": urgency,
+            "problem_type": problem_type,
+            "key_words": key_words,
+            "expected_detail": expected_detail,
+            "time_gap": time_gap,
+            "client_expectation": client_expectation,
+            "channel": channel,
+            "power_relationship": power_relationship
+        })
+
+    return output
+
+# Streamlit app layout
+st.title("Conversation Labeling App")
+st.write("Upload a JSON file containing conversations in the specified format.")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose a JSON file", type="json")
+
+if uploaded_file is not None:
+    # Read the uploaded file
+    data = json.load(uploaded_file)
+    
+    if "conversation" in data:
+        # Analyze the conversation and generate the output
+        result = analyze_conversation(data["conversation"])
+        
+        # Display the output
+        st.subheader("Labeled Conversation")
+        st.json(result)
+    else:
+        st.error("The uploaded JSON file does not contain a 'conversation' key.")