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app.py

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+
+import os
+import gradio as gr
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.prompts import PromptTemplate
+
+# Initialize the chatbot
+HF_TOKEN = os.getenv("HF_TOKEN")
+llm = HuggingFaceEndpoint(
+    repo_id="google/gemma-1.1-7b-it",
+    task="text-generation",
+    max_new_tokens=512,
+    top_k=5,
+    temperature=0.1,
+    repetition_penalty=1.03,
+    huggingfacehub_api_token=HF_TOKEN
+)
+template = """
+You are a Mental Health Chatbot. Help the user with their mental health concerns. 
+Use the context below to answer the questions {context} 
+Question: {question} 
+Helpful Answer:"""
+QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],template=template)
+
+def predict(message, history):
+    input_prompt = QA_CHAIN_PROMPT.format(question=message, context=history)
+    result = llm.generate([input_prompt])
+    print(result)  # Print the result for inspection
+    
+    # Access the generated text using the correct attribute(s)
+    if result.generations:
+        ai_msg = result.generations[0][0].text
+    else:
+        ai_msg = "I'm sorry, I couldn't generate a response for that input."
+    
+    return ai_msg
+
+
+gr.ChatInterface(predict).launch()

requirements.txt

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+langchain
+gradio
+langchain-community
+chromadb
+wandb
+transformers